Institute for Mathematics and its Applications University of Minnesota 114 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
20102011 Program
See http://www.ima.umn.edu/20102011/ for a full description of the 20102011 program on Simulating Our Complex World: Modeling, Computation and Analysis.
20102011 IMA Participating Institutions Conferences
10:45am11:15am  Coffee break  Lind Hall 400 
10:45am11:15am  Coffee break  Lind Hall 400  
2:00pm3:00pm  Clawpack tutorial  Randall J. Leveque (University of Washington)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Multigrid methods for Maxwell’s equations  Jintao Cui (University of Minnesota)  Lind Hall 305  PS 
2:00pm3:00pm  Clawpack tutorial  Randall J. Leveque (University of Washington)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
2:00pm3:00pm  Clawpack tutorial  Randall J. Leveque (University of Washington)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Special course: Finite element exterior calculus  Douglas N. Arnold (University of Minnesota)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
1:25pm2:25pm  Challenges and solutions in clinical image analysis  Osama Masoud (ViTAL Images, Inc.)  Vincent Hall 16  IPS 
10:45am11:15am  Coffee break  Lind Hall 400  
2:00pm5:00pm  Tutorial: Deal. II Finite element library  Guido Kanschat (Texas A & M University)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Hierarchical approximations, coarsegraining and fast lattice Monte Carlo simulations  Petr Plechac (University of Tennessee)  Lind Hall 305  PS 
10:45am11:15am  Coffee break  Lind Hall 400  
2:00pm5:00pm  Tutorial: Deal. II Finite element library  Guido Kanschat (Texas A & M University)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Special course: Finite element exterior calculus  Douglas N. Arnold (University of Minnesota)  Lind Hall 305  
3:30pm4:30pm  School of Math colloquium: Nonconforming finite element methods for the Maxwell eigenproblem  Susanne C. Brenner (Louisiana State University)  Vincent Hall 16 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Calculating storm surge and other coastal hazards using Geoclaw  Kyle Mandli (University of Washington)  Lind Hall 401  
1:25pm2:25pm  A Novel approach to tight bounds and statistical information of rounding errors  Peter Tang (D. E. Shaw Research)  Vincent Hall 16  IPS 
8:45am9:00am  Coffee  Keller Hall 3176  T10.1617.10  
9:00am10:30am  Random lsc functions and expectation functionals  Roger J.B. Wets (University of California, Davis)  Keller Hall 3180  T10.1617.10 
10:30am11:00am  Break  Keller Hall 3176  T10.1617.10  
11:00am12:00pm  Introduction to the calculus of expectation functionals  Roger J.B. Wets (University of California, Davis)  Keller Hall 3180  T10.1617.10 
12:00pm1:30pm  Lunch  T10.1617.10  
1:30pm2:15pm  Lecture 4. Elliptic equations with random input parameters: regularity results; convergence analysis for Galerkin and Collocation approximations. Anisotropic approximations  Raul F. Tempone (King Abdullah University of Science & Technology)  Keller Hall 3180  T10.1617.10 
2:15pm2:30pm  Break  Keller Hall 3176  T10.1617.10  
2:30pm3:15pm  Lecture 5. Numerical examples, numerical comparison of SGM and SCM. Adaptive approximation  Raul F. Tempone (King Abdullah University of Science & Technology)  Keller Hall 3180  T10.1617.10 
3:15pm3:30pm  Break  Keller Hall 3176  T10.1617.10  
3:30pm4:15pm  Lecture 6. The infinite dimensional case  Christoph Schwab (ETH Zürich)  Keller Hall 3180  T10.1617.10 
8:30am9:15am  Registration and coffee  Keller Hall 3176  W10.1822.10  
9:15am9:30am  Welcome to the IMA  Fadil Santosa (University of Minnesota)  Keller Hall 3180  W10.1822.10 
9:30am10:30am  Porous flow as a high dimensional challenge  Ian H. Sloan (University of New South Wales)  Keller Hall 3180  W10.1822.10 
10:30am11:00am  Coffee break  Keller Hall 3176  W10.1822.10  
11:00am12:00pm  Monte Carlo sampling techniques for solving stochastic and large scale deterministic optimization problems  Alexander Shapiro (Georgia Institute of Technology)  Keller Hall 3180  W10.1822.10 
12:00pm2:00pm  Lunch  W10.1822.10  
2:00pm3:00pm  Stochastic models with application to approximation of optimization problems  Christian Louis Hess (Université de ParisDauphine)  Keller Hall 3180  W10.1822.10 
3:00pm4:00pm  Generating and handling scenarios in stochastic programming  Werner Römisch (HumboldtUniversität)  Keller Hall 3180  W10.1822.10 
4:00pm4:30pm  Coffee break  Keller Hall 3176  W10.1822.10  
4:30pm5:30pm  Multiresolution stochastic Galerkin methods for uncertain hyperbolic flows  Olivier Pierre Le Maître (Centre National de la Recherche Scientifique (CNRS))  Keller Hall 3180  W10.1822.10 
8:10am8:30am  Coffee  Keller Hall 3176  W10.1822.10  
8:30am9:30am  Quantifying uncertainty in climate change science: Empirical information theory, fluctuation dissipation theorems, and physics based statistics  Andrew J. Majda (New York University)  Keller Hall 3180  W10.1822.10 
9:30am10:30am  Tools for analyzing variational models  Stephen Michael Robinson (University of Wisconsin)  Keller Hall 3180  W10.1822.10 
10:30am11:00am  Coffee break  Keller Hall 3176  W10.1822.10  
11:00am12:00pm  Complexity and heuristics in stochastic optimization  Teemu Pennanen (Helsinki University of Technology)  Keller Hall 3180  W10.1822.10 
12:00pm2:00pm  Lunch  W10.1822.10  
2:00pm3:00pm  Progressive hedging for multistage stochastic optimization problems  JeanPaul Watson (Sandia National Laboratories) David L. Woodruff (University of California, Davis)  Keller Hall 3180  W10.1822.10 
3:00pm3:10pm  Group photo  (steps of Lind Hall in front of courtyard).  W10.1822.10  
3:10pm4:20pm  Free time for discussion  Keller Hall 3180  W10.1822.10  
4:30pm6:00pm  Reception and Poster Session Poster submissions welcome from all participants Instructions  Lind Hall 400  W10.1822.10  
Parametric eigenvalue problems  Roman Andreev (ETH Zürich)  
Discrete adapted hierarchical basis solver for the large scale radial basis function interpolation problem with applications to the best linear unbiased estimator  Julio Enrique Castrillon Candas (King Abdullah University of Science & Technology)  
Sparse polynomial approximation for elliptic equations with random loading  Alexey Chernov (Rheinische FriedrichWilhelmsUniversität Bonn)  
Curse of dimensionality and lowrank approximations in stochastic mechanics  Alireza Doostan (University of Colorado)  
Efficient uncertainty quantification using GPUs  Gaurav Gaurav (University of Minnesota)  
Adaptive stochastic Galerkin methods  Claude Jeffrey Gittelson (ETH)  
Coupled coarse grained MCMC methods for stochastic lattice systems  Evangelia Kalligiannaki (University of Tennessee) Markos A. Katsoulakis (University of Massachusetts) Petr Plechac (University of Tennessee)  
A computable weak error expansion for the tauleap method  Jesper Karlsson (King Abdullah University of Science & Technology)  
Uncertainty quantification & dynamic state estimation for power systems  Guang Lin (Pacific Northwest National Laboratory)  
Implications of the constant rank constraint qualification  Shu Lu (University of North Carolina)  
Derivation of DBN structure from expert knowledge in the form of systems of ODEs  Niall Madden (National University of Ireland, Galway)  
A worstcase robust design optimization methodology based on distributional assumptions  Mattia Padulo (National Aeronautics and Space Administration (NASA))  
Stochastic parametrizations and simulations in porous media  Malgorzata Peszynska (Oregon State University)  
Multiscale stochastic optimization with applications in energy systems planning  Suvrajeet Sen (Ohio State University)  
Efficient uncertainty quantification for experiment design in sparse Bayesian models  Florian Steinke (Siemens)  
PySP: Stochastic programming in Python  JeanPaul Watson (Sandia National Laboratories) David L. Woodruff (University of California, Davis)  
Pyomo: An opensource tool for modeling and solving mathematical programs  JeanPaul Watson (Sandia National Laboratories) David L. Woodruff (University of California, Davis)  
Tool path planning with dual spherical spline  Yayun Zhou (Siemens)  
Adaptive multi level Monte Carlo simulation  Erik von Schwerin (King Abdullah University of Science & Technology) 
8:10am8:30am  Coffee  Keller Hall 3176  W10.1822.10  
8:30am9:30am  Accounting for variability and uncertainty in a complex brain metabolic model via a probabilistic framework  Daniela Calvetti (Case Western Reserve University)  Keller Hall 3180  W10.1822.10 
9:30am10:30am  Validating models of complex physical systems and associated uncertainty models  Robert D. Moser (University of Texas at Austin)  Keller Hall 3180  W10.1822.10 
10:30am11:00am  Coffee break  Keller Hall 3176  W10.1822.10  
11:00am12:00pm  Panel Session: "Uncertainty in PDEs and optimizations, interations, synergies, challenges"
Moderator: Suvrajeet Sen (Ohio State University)  Timothy J. Barth (NASA Ames Research Center) Omar Ghattas (University of Texas at Austin) Alejandro Rene Jofre (University of Chile) Robert P. Lipton (Louisiana State University) Stephen Michael Robinson (University of Wisconsin)  Keller Hall 3180  W10.1822.10 
12:00pm4:30pm  Lunch/Afternoon free  W10.1822.10  
6:30pm8:30pm  Workshop dinner at Tea House  Tea House 2425 University Ave. SE, Mpls MN 55414 6123318866 
W10.1822.10 
8:10am8:30am  Coffee  Keller Hall 3176  W10.1822.10  
8:30am9:30am  Weak Convergence of Numerical Methods for Dynamical Systems and Optimal Control, and a relation with Large Deviations for Stochastic Equations  Mattias Sandberg (Royal Institute of Technology (KTH))  Keller Hall 3180  W10.1822.10 
9:30am10:30am  Measures of risk in stochastic optimization  R. Tyrrell Rockafellar (University of Washington)  Keller Hall 3180  W10.1822.10 
10:30am11:00am  Coffee break  Keller Hall 3176  W10.1822.10  
11:00am12:00pm  An extended mathematical programming framework  Michael C. Ferris (University of Wisconsin)  Keller Hall 3180  W10.1822.10 
12:00pm2:00pm  Lunch  W10.1822.10  
2:00pm3:00pm  Second moment analysis of elliptic problems with stochastic input parameters  Helmut Harbrecht (Universität Stuttgart)  Keller Hall 3180  W10.1822.10 
3:00pm4:00pm  Short Lectures  Keller Hall 3180  W10.1822.10  
Robust estimates for stochastic discretetime nonlinear systems (robust Kalman filtering/smoothing)  Aleksandr Yakovlevitch Aravkin (University of Washington)  
Do electricity markets generate electricity inefficiently?  Andy Philpott (University of Auckland)  
On the need for uncertainty quantification in hyperbolic PDE applications at Sandia National Laboratories  Guglielmo Scovazzi (Sandia National Laboratories)  
A stochastic programming groundwater remediation — flow/transport through porous media  JeanPaul Watson (Sandia National Laboratories)  
4:00pm4:30pm  Coffee break  Keller Hall 3176  W10.1822.10  
4:30pm6:30pm  Discussion  Keller Hall 3180  W10.1822.10 
8:10am8:30am  Coffee  Keller Hall 3176  W10.1822.10  
8:30am9:30am  Accelerated kinetic Monte Carlo methods: Hierarchical parallel algorithms and coarsegraining  Markos A. Katsoulakis (University of Massachusetts)  Keller Hall 3180  W10.1822.10 
9:30am10:30am  Model reduction for uncertainty quantification and optimization under uncertainty of largescale complex systems  Karen E. Willcox (Massachusetts Institute of Technology)  Keller Hall 3180  W10.1822.10 
10:30am11:00am  Coffee break  Keller Hall 3176  W10.1822.10  
11:00am12:00pm  Multiscale structural optimization in the presence of uncertainty for very large composite structures  Robert P. Lipton (Louisiana State University)  Keller Hall 3180  W10.1822.10 
10:45am11:15am  Coffee break  Lind Hall 400 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Simulating nonholonomic mechanics using variational integrators through Hamiltonization  Oscar E. Fernandez (University of Minnesota)  Lind Hall 305  PS 
12:15pm1:15pm  Postdoc Lunch  Lind Hall 409 
10:45am11:15am  Coffee break  Lind Hall 400 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Special course: Finite element exterior calculus  Douglas N. Arnold (University of Minnesota)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
1:25pm2:25pm  Predictive modeling of mental states from fMRI data  Irina Rish (IBM)  Vincent Hall 16  IPS 
All Day  Chair: Susanne C. Brenner (Louisiana State University)  T10.3031.10  
12:30pm1:30pm  Registration and coffee  Keller Hall 3176  T10.3031.10  
1:30pm3:00pm  Tutorial on HDG methods  Bernardo Cockburn (University of Minnesota)  Keller Hall 3180  T10.3031.10 
3:00pm3:30pm  Break  Keller Hall 3176  T10.3031.10  
3:30pm5:00pm  Introduction to finite element exterior calculus  Ragnar Winther (University of Oslo)  Keller Hall 3180  T10.3031.10 
All Day  Morning Chair: Claudio Canuto (Politecnico di Torino) Afternoon Chair: Susanne C. Brenner (Louisiana State University)  T10.3031.10  
8:45am9:00am  Coffee  Keller Hall 3176  T10.3031.10  
9:00am10:30am  Recent advances in mutliscale finite element methods  Thomas Yizhao Hou (California Institute of Technology)  Keller Hall 3180  T10.3031.10 
10:30am11:00am  Break  Keller Hall 3176  T10.3031.10  
11:00am12:30pm  Fast spectralGalerkin methods: from one dimension to high dimension  Jie Shen (Purdue University)  Keller Hall 3180  T10.3031.10 
12:30pm2:00pm  Lunch  T10.3031.10  
2:00pm3:30pm  Leastsquares methods for PDEs: A fair and balanced perspective  Pavel B. Bochev (Sandia National Laboratories)  Keller Hall 3180  T10.3031.10 
3:30pm4:00pm  Break  Keller Hall 3176  T10.3031.10  
4:00pm5:30pm  Reduced complexity models you can believe in  Jan S. Hesthaven (Brown University)  Keller Hall 3180  T10.3031.10 
Event Legend: 

IPS  Industrial Problems Seminar 
PS  IMA Postdoc Seminar 
T10.1617.10  Computing with Uncertainty 
T10.3031.10  Tutorials on Some Novel Discretization Techniques 
W10.1822.10  Computing with Uncertainty: Mathematical Modeling, Numerical Approximation and Large Scale Optimization of Complex Systems with Uncertainty 
Roman Andreev (ETH Zürich)  Parametric eigenvalue problems 
Abstract: We design and analyze algorithms for the efficient sensitivity computation of eigenpairs of parametric elliptic selfadjoint eigenvalue problems (EVPs) on highdimensional parameter spaces. We quantify the analytic dependence of eigenpairs on the parameters. For the efficient evaluation of parameter sensitivities of isolated eigenpairs on the entire parameter space we propose and analyze a sparse tensor spectral collocation method on an anisotropic sparse g rid Applications include elliptic EVPs with countably many parameters arising from elliptic differential operators with random coefficients.  
Aleksandr Yakovlevitch Aravkin (University of Washington)  Robust estimates for stochastic discretetime nonlinear systems (robust Kalman filtering/smoothing) 
Abstract: No Abstract  
Timothy J. Barth (NASA Ames Research Center), Omar Ghattas (University of Texas at Austin), Alejandro Rene Jofre (University of Chile), Robert P. Lipton (Louisiana State University), Stephen Michael Robinson (University of Wisconsin)  Panel Session: "Uncertainty in PDEs and optimizations, interations, synergies, challenges"
Moderator: Suvrajeet Sen (Ohio State University) 
Abstract: No Abstract  
Pavel B. Bochev (Sandia National Laboratories)  Leastsquares methods for PDEs: A fair and balanced perspective 
Abstract: In this lecture I will present an unconventional perspective on leastsquares finite element methods, which connects them to compatible methods and shows that leastsquares methods can enjoy the same conservation properties as their mixed Galerkin cousins. To a casual observer, compatible (or mimetic) methods and least squares principles for PDEs couldn't be further apart. Mimetic methods inherit key conservation properties of the PDE, can be related to a naturally occurring optimization problem, and require specially selected, dispersed degrees of freedom. The conventional wisdom about least squares is that they rely on artificial energy principles, are only approximately conservative, but can work with standard C^{0} nodal (or collocated) degrees of freedom. The latter is considered to be among the chief reasons to use least squares methods. This lecture demonstrates that exactly the opposite is true about leastsquares methods. First, I will argue that nodal elements, while admissible in least squares, do not allow them to realize their full potential, should be avoided and are, perhaps, the least important reason to use least squares! Second, I will show that for an important class of problems least squares and compatible methods are close relatives that share a common ancestor, and in some circumstances compute identical answers. The price paid for gaining favorable conservation properties is that one has to give up what is arguably the least important advantage attributed to least squares methods: one can no longer use C^{0} nodal elements for all variables. If time permits I will explore two other unconventional uses of leastsquares ideas which result in numerical schemes with attractive computational properties: a leastsquares meshtying method that passes patch tests of arbitrary orders, and a locally conservative discontinuous velocity leastsquares method for incompressible flows. The material in this talk is drawn from collaborative works with M. Gunzburger (FSU), M Hyman (Tulane), L. Olson (UIUC) and J. Lai (UIUC). Sandia National Laboratories is a multiprogram laboratory operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin company, for the U.S. Department of Energy's National Nuclear Security Administration under contract DEAC0494AL85000. 

Susanne C. Brenner (Louisiana State University)  School of Math colloquium: Nonconforming finite element methods for the Maxwell eigenproblem 
Abstract: The space of H(curl) vector fields with zero
divergence provides a natural setting for the
the Maxwell eigenproblem with a perfectly conducting
boundary. The variational formulation of the Maxwell
eigenproblem based on this space is automatically
free of spurious eigenvalues. However, a finite element
subspace of the intersection of H(curl) and H(div) is
necessarily a subspace of H1 vector fields, and it is also
known that H1 vector fields are not dense in the intersection
of these two spaces unless the domain is convex.
Consequently it is impossible to have an H(curl) and
H(div) conforming method for the Maxwell eigenproblem
that works on nonconvex domains. In this talk we
will discuss nonconforming finite element methods
that can overcome this difficulty. 

Daniela Calvetti (Case Western Reserve University)  Accounting for variability and uncertainty in a complex brain metabolic model via a probabilistic framework 
Abstract: In this talk we propose a probabilistic interpretation of the parameters in the system of differential equations describing a complex cellular brain metabolism model. Uncertainty in the parameter values, variability of the data over a population and errors in the collected data contribute to the variance of the distributions of the parameters. Markov chain Monte Carlo sampling schemes are employed to draw parameter sets which identify models in statistical agreement with the available data and with the a priori belief about the system. The ensemble of solutions of the differential equations corresponding to the different parameter sets provides a measure of how uncertainty in the parameters translates into variability of the predictive output of the model. 

Julio Enrique Castrillon Candas (King Abdullah University of Science & Technology)  Discrete adapted hierarchical basis solver for the large scale radial basis function interpolation problem with applications to the best linear unbiased estimator 
Abstract: We develop an adapted discrete Hierarchical Basis (HB) to stabilize and efficiently solve the Radial Basis Function (RBF) interpolation problem with finite polynomial order. Applications to the the Best Linear Unbiased Estimator regression problem are shown. The HB forms an orthonormal set that is orthogonal to the space of polynomials of order m defined on the set of nodes in 3D. This leads to the decoupling of the RBF problem thus removing the polynomial illconditioning dependency from the joint problem. In particular, the adapted HB method works well for higherorder polynomials.  
Alexey Chernov (Rheinische FriedrichWilhelmsUniversität Bonn)  Sparse polynomial approximation for elliptic equations with random loading 
Abstract: Numerical approximation of functions in high dimensions is a hard task;
e.g. the classical tensor approximation leads to the computational cost
and storage requirements growing exponentially with the dimension d
("curse of dimensionality"). However, under the mixed regularity
assumption, an efficient approximation via the Sparse Grid techniques is
possible. In the context of classical SG, developed by Zenger, Griebel,
et al. the polynomial degree of the FE basis functions is fixed and the
convergence is achieved by the hierarchical refinement of their support,
like in the hversion FEM. Extending the approach of Temlyakov for the
periodic case, in [1,2] we aim at the construction and analysis of the
sparse polynomial discretization in spirit of the pversion FEM, where
the support of the FE basis functions is fixed and the convergence is
achieved by increasing the polynomial degree subjected to a hyperbolic
cross type restriction. Extending results in [1] for L2 and negative
order Sobolev spaces, we obtain in [2] the optimal a priori convergence
rates in positive order Sobolev spaces, possibly with homogeneous
Dirichlet boundary conditions. One application of this approximation
result is the sparse polynomial approximation of statistical moments of
solutions of elliptic equations with a random loading term. This poster is partially based on joint work with Christoph Schwab. [1] A. Chernov and C. Schwab, Sparse pversion BEM for first kind boundary integral equations with random loading, Applied Numerical Mathematics 59 (2009) 2698–2712 [2] A. Chernov, Sparse polynomial approximation in positive order sobolev spaces with bounded mixed derivatives and applications to elliptic problems with random loading, Preprint 1003, Institute for Numerical Simulation, University of Bonn, 2010 

Bernardo Cockburn (University of Minnesota)  Tutorial on HDG methods 
Abstract: In this tutorial, we will present the hybridizable discontinuous Galerkin (HDG) methods for diffusion problems. We will describe the main idea for devising them and will explain how to implement them efficiently. We will then compare the methods with mixed methods and the continuous Galerkin methods. Finally, we will discuss the convergence properties of the methods in terms of their stabilization parameters.  
Jintao Cui (University of Minnesota)  Multigrid methods for Maxwell’s equations 
Abstract: In this work we study finite element methods for twodimensional Maxwell’s equations and their solutions by multigrid algorithms. We first introduce two types of nonconforming finite element methods on graded meshes for a twodimensional curlcurl and graddiv (CCGD) problem that appears in electromagnetics. The first method is based on a discretization using weakly continuous P_{1} vector fields. The second method uses discontinuous P_{1} vector fields. Optimal convergence rates in the energy norm and the L_{2} norm are established for both methods on graded meshes. Then we consider a class of symmetric discontinuous Galerkin methods for a model Poisson problem on graded meshes that share many techniques with the nonconforming methods for the CCGD problem. We establish the uniform convergence of Wcycle, Vcycle and Fcycle multigrid algorithms for the resulting discrete problems. Finally, we propose a new numerical approach for twodimensional Maxwell’s equations that is based on the Hodge decomposition for divergencefree vector fields, and present multigrid results.  
Alireza Doostan (University of Colorado)  Curse of dimensionality and lowrank approximations in stochastic mechanics 
Abstract: This is a joint work with Gianluca Iaccarino (Stanford University). This work is concerned with the efficiency of some existing uncertainty propagation schemes for the solution of stochastic partial differential equations (SPDEs) with large number of input uncertain parameters. The uncertainty quantification schemes based on stochastic Galerkin projections, with global or local basis functions, and also sparse grid collocations, in their conventional form, suffer from the so called curse of dimensionality: the associated computational cost grows exponentially as a function of the number of random variables defining the underlying probability space of the problem. In this work, to break the problem of curse of dimensionality, an efficient leastsquares scheme is utilized to obtain a lowrank approximation of the solution of an SPDE with highdimensional random input data. It will be shown that, in theory, the computational cost of the proposed algorithm grows linearly with respect to the dimension of the underlying probability space of the system. Different aspects of the proposed methodology are clarified through its application to a convectiondiffusion problem. 

Oscar E. Fernandez (University of Minnesota)  Simulating nonholonomic mechanics using variational integrators through Hamiltonization 
Abstract: Although it is well known that nonholonomic mechanical systems are not Hamiltonian, recent research has uncovered a variety of techniques which allow one to express the reduced, constrained dynamics of certain classes of nonholonomic systems as Hamiltonian. In this talk I will discuss the application of these methods to develop alternative geometric integrators for nonholonomic systems with perhaps more eacuteciency than the known nonholonomic integrators. After showing how variational integrators theoretically preserve conserved mechanical quantities (such as momentum and energy), I will discuss how Hamiltonization can be used to apply these variational integrators to certain classes of nonholonomic systems. Finally, I will discuss some current research utilizing time reparameterizations.  
Michael C. Ferris (University of Wisconsin)  An extended mathematical programming framework 
Abstract: Coauthors: Steven Dirkse, Jan Jagla, Alexander Meeraus. Traditional modeling approaches for mathematical programs have limitations. We outline a mechanism to describe an extended mathematical program by means of annotating existing relationships that make up a model. These extensions facilitate higher level structure identification within a model. The structures, which often involve constraints on the solution sets of other models, disjunctions, variational inequalities or complementarity relationships, can be exploited by modern large scale mathematical programming algorithms for efficient solution. Specific application to a variety of models will be given. 

Gaurav Gaurav (University of Minnesota)  Efficient uncertainty quantification using GPUs 
Abstract: Joint work with Steven F. Wojtkiewicz ( Department of Civil Engineering, University of Minnesota). Graphics processing units (GPUs) have emerged as a much economical and a highly competitive alternative to CPUbased parallel computing. Recent studies have shown that GPUs consistently outperform their best corresponding CPUbased parallel computing equivalents by up to two orders of magnitude in certain applications. Moreover, the portability of the GPUs enables even a desktop computer to provide a teraflop (10^{12} floating point operations per second) of computing power. This study presents the gains in computational efficiency obtained using the GPUbased implementations of five types of algorithms frequently used in uncertainty quantification problems arising in the analysis of dynamical systems with uncertain parameters and/or inputs. 

Claude Jeffrey Gittelson (ETH)  Adaptive stochastic Galerkin methods 
Abstract: We consider stochastic Galerkin methods for elliptic PDE depending on a random field. Expanding this field into a series with independent coefficients introduces an infinite product structure on the probability space. This permits a discretization by tensor products of suitable orthonormal polynomials. The original problem can be reformulated as an infinite system of equations for the coefficients of the solution with respect to this basis. Without any truncation of the series, restricting to a finite set of polynomial basis functions reduces this infinite system to a finite system of deterministic equations, which can be solved by standard finite element methods. The only remaining challenge is the selection of active basis functions. We tackle this problem by iterative methods based on adaptive wavelet techniques. Our method uses adaptive local truncation of the series expansion to recursively refine the set of active indices. These results are part of a PhD thesis under the supervision of Prof. Ch. Schwab, supported in part by the Swiss National Science Foundation under grant No. 200021120290/1. 

Helmut Harbrecht (Universität Stuttgart)  Second moment analysis of elliptic problems with stochastic input parameters 
Abstract: We compute the expectation and the twopoint correlation of the solution to elliptic boundary value problems with stochastic input data. Besides stochastic loadings, via perturbation theory, our approach covers also elliptic problems on stochastic domains or with stochastic coefficients. The solution's twopoint correlation satisfies a deterministic boundary value problem with the twofold tensor product operator on the twofold tensor tensor product domain. We discuss the efficient solution of such tensor product problems by either a sparse grid approach based on multilevel frames or by the pivoted Cholesky decomposition. Both approaches involve only standard finite element techniques. Numerical results illustrate the algorithms. 

Christian Louis Hess (Université de ParisDauphine)  Stochastic models with application to approximation of optimization problems 
Abstract: In this lecture it will be shown how basic concepts of
Probability Theory, such as distribution, independence,
(conditional) expectation, can be extended to the case of
random sets and random (lower semicontinuous)
functions. Then, some convergence results for sequences of
random sets and random functions, already known for
sequences or realvalued random variables, will be presented.
It will be also shown how these results give rise to
various applications to the convergence or approximation of
some optimization problems.
Plan


Jan S. Hesthaven (Brown University)  Reduced complexity models you can believe in 
Abstract: The development and application of models of reduced computational
complexity is used extensively throughout science and engineering to
enable the fast/realtime modeling of complex systems for control,
design, or prediction purposes. These models, while often successful
and of undisputed value, are, however, often heuristic in nature and
the validity and accuracy of the output is often unknown. This limits
the predictive value of such models. In this tutorial we will review recent and ongoing efforts to develop reduced basis methods for which one can develop a rigorous a posteriori theory. The approach aims at formulating reduced models for parameterized linear partial differential equations. We will outline the theoretical developments of certified reduced basis methods, discuss an offlineonline approach to ensure computational efficiency, and emphasize how an error estimator can be exploited to construct an efficient basis at minimal computational offline cost. We also discuss recent improvements on the efficiency of the computation of the lower bounds for the error, using an improved Successive Constraint Method. The discussion will draw on examples based both on differential and integral equations formulations. The performance of the certified reduced basis model will be illustrated through several examples to highlight the major advantages of the proposed approach as well as key open challenges in the current approach. Time permitting we will extend the discussion to include problems with parameterized geometries and the introduction of reduced element methods to enable the efficient and accurate modeling of networks and geometrically complex configurations. 

Thomas Yizhao Hou (California Institute of Technology)  Recent advances in mutliscale finite element methods 
Abstract: A broad range of scientific and engineering problems involve multiple scales. Traditional approaches have been known to be valid for limited spatial and temporal scales. Multiple scales dominate simulation efforts wherever large disparities in spatial and temporal scales are encountered. Such disparities appear in virtually all areas of modern science and engineering, for example, composite materials, porous media, turbulent transport in high Reynolds number flows, and so on. Here, we review some recent advances in multiscale finite element methods (MsFEM) and their applications. The notion ``multiscale finite element methods'' refers to a number of methods, such as multiscale finite volume, mixed multiscale finite element method, and the like. The concept that unifies these methods is the coupling of oscillatory basis functions via various variational formulations. One of the main aspects of this coupling is the subgrid capturing errors. We attempt to capture the multiscale structure of the solution via localized basis functions. These basis functions contain essential multiscale information embedded in the solution and are coupled through a global formulation to provide a faithful approximation of the solution. The lecture will start with some basic ideas behind MsFEM and its error analysis. We will put special emphasis on how to design appropriate boundary conditions for the local bases to minimize the subgrid capturing errors. In some cases, limited global information is required to capture the long range correlation among small scales. One way to achieve this is through an iterative precodure between the global large scale solution and the localized subgrid scale solution. We will also compare MsFEM with a few related multiscale methods. Applications to high contrast interface problems, twophase flows in strongly heterogeneous porous media, uncertainty quantification, and domain decompositions will be discussed. Finally, we will present a new datadriven stochastic multiscale method for solving stochastic PDEs, which is in part inspired by MsFEM. 

Evangelia Kalligiannaki (University of Tennessee), Markos A. Katsoulakis (University of Massachusetts), Petr Plechac (University of Tennessee)  Coupled coarse grained MCMC methods for stochastic lattice systems 
Abstract: We propose a class of Monte Carlo methods for sampling dynamic and equilibrium properties of stochastic lattice systems with complex interactions. The key ingredient of these methods is that each MC step is composed by two properly coupled MC steps efficiently coupling coarse and microscoscopic state spaces, designed in virtue of coarse graining techniques for lattice systems. We achieve significant reduction of the computational cost of traditional Markov Chain Monte Carlo and kinetic Monte Carlo methods for systems with competing interactions, while capable of providing microscopic information.  
Guido Kanschat (Texas A & M University)  Tutorial: Deal. II Finite element library 
Abstract: In the first part I will introduce deal.II and discuss its capabilities and limitation. I will give an overview of the development paradigms of the library and present the structure of a typical application based on it in order to address the question whether deal.II is the right tool for your purposes or not. The second part of the tutorial focuses on the implementation of basic model problems, following the first six steps of the online tutorial. Starting with generating and refining simple meshes (step 1), we move on to solving Poisson's equation (step 3). We modify the program to study how to use different finite elements, solvers and to implement other bilinear forms. We wrap up by introducing techniques for dimension independent programming, adaptive iterations and multilevel methods. The tutorial closes with the discussion of more advanced applications. We study the handling of systems of equations at hand of the LameNavier equations of elasticity, the (linear) Darcy equations for porous media flow, and the Stokes equations. Participants are welcome to suggest additional applications (possibly in advance). The tutorial is openended and we can continue working on projects during the next months. 

Jesper Karlsson (King Abdullah University of Science & Technology)  A computable weak error expansion for the tauleap method 
Abstract: This work develops novel error expansions with computable leading order terms for the global weak error in the tauleap discretization of pure jump processes arising in kinetic Monte Carlo models. Accurate computable a posteriori error approximations are the basis for adaptive algorithms; a fundamental tool for numerical simulation of both deterministic and stochastic dynamical systems. These pure jump processes are simulated either by the tauleap method, or by exact simulation, also referred to as dynamic Monte Carlo, the Gillespie algorithm or the Stochastic simulation algorithm. Two types of estimates are presented: an a priori estimate for the relative error that gives a comparison between the work for the two methods depending on the propensity regime, and an a posteriori estimate with computable leading order term.  
Markos A. Katsoulakis (University of Massachusetts)  Accelerated kinetic Monte Carlo methods: Hierarchical parallel algorithms and coarsegraining 
Abstract: In this talk we present two intimately related approaches in speedingup molecular simulations via Monte Carlo simulations. First, we discuss coarsegraining algorithms for systems with complex, and often competing particle interactions, both in the equilibrium and nonequilibrium settings, which rely on multilevel sampling and communication. Second, we address mathematical, numerical and algorithmic issues arising in the parallelization of spatially distributed Kinetic Monte Carlo simulations, by developing a new hierarchical operator splitting of the underlying highdimensional generator, as means of decomposing efficiently and systematically the computational load and communication between multiple processors. The common theme in both methods is the desire to identify and decompose the particle system in components that communicate minimally and thus local information can be either described by suitable coarsevariables (coarsegraining), or computed locally on a individual processors within a parallel architecture.  
Olivier Pierre Le Maître (Centre National de la Recherche Scientifique (CNRS))  Multiresolution stochastic Galerkin methods for uncertain hyperbolic flows 
Abstract: We present a multiresolution scheme, based on piecewise polynomial approximations at the stochastic level, for the resolution of nonlinear hyperbolic problems subjected to parametric uncertainties. The numerical method rely on a Galerkin projection technique at the stochastic level, with a finitevolume discretization and a Roe solver (with entropy corrector) in space and time. A key issue in uncertain hyperbolic problem is the loss of smoothness of the solution with regard to the uncertain parameters, which calls for piecewise continuous approximations and multiresolution schemes, together with adaptive strategies. However, discontinuities in the spatial and stochastic domains are well localized, requiring very different discretization efforts according to the local smoothness of the solution. As a result, classical discretization approaches based on the tensorization of stochastic and deterministic approximation spaces (bases) are inefficient and we propose a numerical procedure where the spatial discretization is fixed while the stochastic basis is locally adapted in space to fit the solution complexity. Examples of applications and efficiency / complexity assessment of the method will be shown.  
Randall J. Leveque (University of Washington)  Clawpack tutorial 
Abstract: Clawpack (Conservation Laws Package) is an open source software package for solving hyperbolic systems of partial differential equations in one or more space dimensions, both with and without source terms. Equations of this type appear in a wide variety of wave propagation problems arising in nearly all fields of science and engineering. Applications include acoustics in the atmosphere or ocean, elastic waves such as seismic waves in the earth or ultrasound waves in biological materials, shock waves in aerodynamics or astrophysics, tsunamis and storm surge, detonation waves, traffic jams, and electromagnetic waves such as light pulses. High resolution shockcapturing finite volume methods are implemented in Clawpack on logically rectangular grids (Cartesian or mapped grids). Adaptive mesh refinement capabilities are included. The core routines are in Fortran and the user interface and graphics capabilities have recently been converted to Python. A number of sample applications are included with the code. This tutorial will be a handson demonstration of how to install the package, try out the sample applications, and set up a new problem, with some discussion of the plotting routines and use of adaptive refinement. Documentation and a gallery of some applications can be viewed at http://www.clawpack.org/doc. 

Guang Lin (Pacific Northwest National Laboratory)  Uncertainty quantification & dynamic state estimation for power systems 
Abstract: Experience suggests that uncertainties often play an important role in controlling the stability of power systems. Therefore, uncertainty needs to be treated as a core element in simulating and dynamic state estimation of power systems. In this talk, a probabilistic collocation method (PCM) will be employed to conduct uncertainty quantification of component level power system models, which can provide an error bar and confidence interval on component level modeling of power systems. Numerical results demonstrate that the PCM approach provides accurate error bar with much less computational cost comparing to classic Monte Carlo (MC) simulations. Additionally, a PCM based ensemble Kalman filter (EKF) will be discussed to conduct realtime fast dynamic state estimation for power systems. Comparing with MC based EKF approach, the proposed PCM based EKF implementation can solve the system of stochastic state equations much more efficient. Moreover, the PCMEKF approach can sample the generalized polynomial chaos approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost. Hence, the PCMEKF approach can drastically reduce the sampling errors and achieve a high accuracy at reduced computational cost, compared to the classical MC implementation of EKF. The PCMEKF based dynamic state estimation is tested on multimachine system with various random disturbances. Our numerical results demonstrate the validity and performance of the PCMEKF approach and also indicate the PCMEFK approach can include the full dynamics of the power systems and ensure an accurate representation of the changing states in the power systems.  
Robert P. Lipton (Louisiana State University)  Multiscale structural optimization in the presence of uncertainty for very large composite structures 
Abstract: Modern structures such as airplane wings and wind turbine blades exhibit a hierarchy of sub structures and typically make use of composite materials in their construction. Quantifying uncertainty in the strength and stiffness of composite structural materials is crucial for predicting the service lifetime of the structure. The high cost of experimental tests for largescale hierarchical composite structures is driving a trend toward virtual testing. This requires the development of multiscale numerical methods capable of handling large degrees of freedom spread across different length scales. In this talk we review model reduction strategies for multiscale structural analysis in the presence of uncertainty as well as propose new multiscale approaches that may be useful in predicting service lifetimes. 

Shu Lu (University of North Carolina)  Implications of the constant rank constraint qualification 
Abstract: We consider a parametric set defined by finitely many equality and inequality constraints under the constant rank constraint qualification (CRCQ). The CRCQ generalizes both the linear independence constraint qualification (LICQ) and the polyhedral case, and is also related to the MangasarianFromovitz constraint qualification (MFCQ) in a certain way. It induces some nice properties of the set when the parameter is fixed, and some nice behavior of the setvalued map when the parameter varies. Such properties are useful in analysis of Euclidean projectors onto the set and variational conditions defined over the set.  
Niall Madden (National University of Ireland, Galway)  Derivation of DBN structure from expert knowledge in the form of systems of ODEs 
Abstract: This is joint with with Catherine G. Enright and Michael G. Madden, NUI
Galway. We present a methodology for constructing a Dynamic Bayesian Network (DBN) from a mathematical model in the form of a system of ordinary differential equations. The motivation for the approach comes from a multidisciplinary project centred on the use of DBNs in the modelling of the response of critically ill patients to certain drug therapies. The DBN can be used to account for at least two sources of uncertainty:
In this presentation we investigate the DBN's ability to handle measurement errors by applying it to an abstract model, based on a system of DEs for which the true solution is known. 

Andrew J. Majda (New York University)  Quantifying uncertainty in climate change science: Empirical information theory, fluctuation dissipation theorems, and physics based statistics 
Abstract: This lecture is based on the following papers: 1. A. Majda and B. Gershgorin, 2010: Quantifying Uncertainty in Climate Change Science Through Empirical Information Theory, PNAS in press 2. A. Majda, R. Abramov, B. Gershgorin, "High Skill in Low Frequency Climate Response through Fluctuation Dissipation Theorems Despite Structural Instability," PNAS, January 2010, Vol. 107, no. 2, pp 581  586. 3. B. Gershgorin, A. Majda, "Filtering A Nonlinear SlowFast System with Strong Fast Forcing," Comm. Math. Sci., March 2010, Vol. 8, Issue 1, pp. 6792 4. A. Majda, B. Gershgorin, Y. Yuan, " Low Frequency Response and FluctuationDissipation Theorems: Theory and Practice," JAS, available electronically, April 2010, Vol. 67, pp. 11861201. All papers except the first one can be found on Majda's faculty website.  
Kyle Mandli (University of Washington)  Calculating storm surge and other coastal hazards using Geoclaw 
Abstract: Coastal flows often require the use of methods that can resolve many order of spatial and temporal scales and often these resolution requirements change in time and space. One way to resolve these scales is to take advantage of these dynamic processes and employ adaptive mesh refinement which uses various aspects of the flow to determine the current required mesh refinement. This allows for a significant savings in computation and can lead to the ability to refine further in regions of interest. We have developed a code named GeoClaw which uses adaptive mesh refinement to solve depth averaged equations over complex bathymetry. It is based on the Clawpack software (Conservation Laws Package, www.clawpack.org), designed for solving general nonlinear hyperbolic systems using highresolution shockcapturing finite volume methods on logically rectangular grids. We will present results from an idealized storm surge along with preliminary results involving multilayer depth averaged equations in order to include vertical structure of the surge to improve the accuracy of the model off the continental shelf. 

Osama Masoud (ViTAL Images, Inc.)  Challenges and solutions in clinical image analysis 
Abstract: Medical Imaging continues to be an area of active research with a wide spectrum of interesting problems. The large increase in data size and the risks of radiation dose to patients are among some recent challenges that emerged in the CT scanner world and require solutions in the industry. This presentation will give an overview of some of the problems that come up in the development of advanced medical analysis software that deals with scanner data. The presentation will go into some detail to discuss challenges and solutions with respect to two problems: Brain perfusion calculation and speed optimization of a particular basic operation.  
Robert D. Moser (University of Texas at Austin)  Validating models of complex physical systems and associated uncertainty models 
Abstract: Computational models of complex physical systems are fraught with uncertainties. These include uncertainties in initial or boundary conditions, uncertainties in model parameters and/or the experimental data used to calibrate them and uncertainties arising from imperfections in the models used in the simulations. Mathematical models of these uncertainties and their affects on the quantities the models are intended to be predicted (the quantities of interest or QoI's) are needed. It is also necessary to assess the ability of the models to represent both the physics of the phenomena being predicted and the associated uncertainties, and in particular the ability to predict the QoI's and their uncertainty. However, in the usual situation, the QoI's are not accessible for observation, since otherwise, no computational prediction would be necessary. We thus must use available or attainable observational data (and estimates of their uncertainty) to calibrate the models and evaluate the ability of the models to predict the unobserved QoI's. In this talk, a Bayesian framework for these calibration and validation processes is proposed and applied to several examples. However, a number of conceptual and practical challenges to applying these ideas in complex systems remain, and will be discussed along with possible approaches to address these problems.  
Mattia Padulo (National Aeronautics and Space Administration (NASA))  A worstcase robust design optimization methodology based on distributional assumptions 
Abstract: This poster outlines a novel Robust Design Optimization (RDO) methodology. The problem is reformulated in order to relax, when required, the assumption of normality of objectives and constraints, which often underlies RDO. In the second place, taking into account engineering considerations concerning the risk associated with constraint violation, suitable estimates of tail conditional expectations are introduced in the set of robustness metrics. The methodology is expected to be of significant practical usefulness for Computational Engineering Design, by guiding the construction of robust objective and constraint functions, and enabling the interpretation of the optimization results.  
Teemu Pennanen (Helsinki University of Technology)  Complexity and heuristics in stochastic optimization 
Abstract: Combining recent results on numerical integration and optimization, we derive a polynomial bound on the worst case complexity of a class of static stochastic optimization problems. We then describe a technique for reducing dynamic problems to static ones. The reduction technique is only a heuristic but it can effectively employ good guesses for good solutions. This is illustrated on an 82period problem coming from pension insurance industry. 

Malgorzata Peszynska (Oregon State University)  Stochastic parametrizations and simulations in porous media 
Abstract: Joint work with M. Ossiander and V. Vasylkivska,
Department of Mathematics, Oregon State University. Coefficients of flow and of related phenomena in subsurface are usually poorly known but are rarely smooth. We discuss parametrizations based on KarhunenLoeve, Haar, and other series expansions, for flow data in a model of singlephase flow in porous media. We use these in finite element algorithms to compute moments of variables of interest such as pressures and fluxes. Of interest are discontinuous and multiscale porous media, as well as data generated by standard geostatistics algorithms. 

Andy Philpott (University of Auckland)  Do electricity markets generate electricity inefficiently? 
Abstract: No Abstract  
Petr Plechac (University of Tennessee)  Hierarchical approximations, coarsegraining and fast lattice Monte Carlo simulations 
Abstract: We shall discuss numerical analysis aspects of coarsegraining stochastic particle systems and the connection to acceleration of kinetic Monte Carlo simulations. Mathematical tools developed for error control in microscopic simulations using the coarsegrained stochastic processes and reconstruction of microscopic scales will be presented in connection with accelerating (kinetic) Monte Carlo simulations. On specific examples of lattice as well as offlattice dynamics we demonstrate that computational implementation of constructed hierarchical algorithms results in significant speed up of simulations. The developed framework also leads to new parallel kinetic Monte Carlo algorithms that will be briefly described.  
Irina Rish (IBM)  Predictive modeling of mental states from fMRI data 
Abstract: Traditional fMRI data analyses are mainly focused on discovering
brain activation patterns using standard GLM technique that selects voxels based on their individual correlations with stimuli.However, such massunivariate approach completely ignores voxel interactions that are often essential for understanding brain functions,and can be better captured by an alternative approach  multivariate predictive modeling. This talk summarizes our recent work in this area, with a particular focus on discovering predictive features ("biomarkers") characterizing nonlocal, distributed patterns of brain activity. One example of our approach is discovering predictive subsets of voxels via sparse regression methods such as LASSO and Elastic Net. We discuss several applications, such as predicting mental states of a subject playing a virtualreality videogame in a fMRI scanner, or predicting subject's pain perception in response to a thermal pain stimuli. We find that sparse regression produces highly predictive models that also provide evidence for the distributed nature of neural function. Next, we underscore the importance of distributed activity patterns when exploring predictive information contained in the topology of brain's functional networks. We consider a challenging task of building a discriminative model for schizophrenia, a complex psychiatric disorder that appears to be delocalized, i.e. difficult to attribute to a dysfunction of some particular brain areas. Our findings demonstrate significant advantages the functional network features can provide over both traditional regionofinterest (ROI) approach and local, taskspecific linear activations produced by standard GLM. Our results suggest that schizophrenia is indeed associated with disruption of global brain properties related to its functioning as a network, which cannot be explained just by alteration of local activation patterns. Moreover, further exploitation of voxel interactions by sparse Markov Random Field (MRF) classifiers allows to attain a high predictive accuracy of 86% over 50% baseline, which is quite remarkable given that our discriminative model is based on a single fMRI experiment using a simple auditory task. 

Stephen Michael Robinson (University of Wisconsin)  Tools for analyzing variational models 
Abstract: Many problems of optimization and equilibrium result in models in the general class of variational conditions, sometimes in a generalized form. Thus, if the problem is one of optimization, we first write optimality conditions and then try to compute with those. If instead of an optimization model we have a model involving some kind of equilibrium, then we write conditions expressing the equilibrium situation and try to solve those conditions. In general, such conditions will involve nonsmoothness (discontinuities in the first derivative) in an essential way. This lecture will present a set of mathematical tools useful for analysis of many of the variational conditions that appear in the formulation and solution of practical problems. In essence, these enable us to do in the presence of nonsmoothness many of the things that one could do with calculus if the problem functions were smooth. They do so by exploiting the fact that the nonsmoothness in these conditions is of a highly structured kind. Although some fairly substantial mathematical analysis underlies the construction of these tools, our emphasis in this lecture will not be on the underlying mathematics. Rather, it will be on explaining what the tools are, how they are adapted to the forms of the variational conditions occurring in various problems, what they can do when applied to those conditions, and how to apply them in some example cases. We will describe the mathematical foundation and indicate how it supports the tools' capabilities, but will not go into much detail about it. 

R. Tyrrell Rockafellar (University of Washington)  Measures of risk in stochastic optimization 
Abstract: A fundamental difficulty in stochastic optimization is the fact that
decisions may not be able pin down the values of future "costs," but
rather can only, within limits, shape their distributions as random variables.
An upper bound on a ramdom "cost" is often impossible, or too expensive, to
enforce with certainty, and so some compromise attitude must be taken to
the violations that might occur. Similarly, there is no instant
interpretation of what it might mean to minimize a random "cost", apart
from trying to determine a lowest threshold which would be exceeded only
to an acceptable degree. Clearly, it is essential in this picture to have a theoretical framework which provides guidelines about preferences and elucidates their mathematical pros and cons. Measures of risk, coming from financial mathematics but finding uses also in engineering, are the key. Interestingly, they relate also to concepts in statistics and estimation. For example, standard deviation can be replaced by a generalized measure of deviation which is not symmetric between ups and downs, as makes sense in applications in which overestimation may be riskier than underestimation. 

Werner Römisch (HumboldtUniversität)  Generating and handling scenarios in stochastic programming 
Abstract: First, three approaches to scenario generation besides Monte Carlo methods are considered: (i) Optimal quantization of probability distributions, (ii) QuasiMonte Carlo methods and (iii) Quadrature rules based on sparse grids. The available theory is discussed and related to applying them in stochastic programming. Second, the problem of optimal scenario reduction and the generation of scenario trees for multistage models are addressed.  
Mattias Sandberg (Royal Institute of Technology (KTH))  Weak Convergence of Numerical Methods for Dynamical Systems and Optimal Control, and a relation with Large Deviations for Stochastic Equations 
Abstract: I will present a method to prove weak convergence of numerical methods for dynamical systems, using dual solutions. This general method is applied to optimal control problems, and is used to prove convergence of approximate value functions. The theory of large deviations will also be mentioned. It makes it possible to represent rare event solutions to stochastic differential equations as solutions of optimal control problems. This representation will be used on a particular stochastic partial differential equation arising in the study of phase transitions. It will be shown how the resulting optimal control problem can be analyzed, again with the same kind of method to prove weak convergence. 

Christoph Schwab (ETH Zürich)  Lecture 1.Problem formulation; examples of elliptic, parabolic, hyperbolic equations with stochastic data; well posedness; the case of infinite dimensional input data (random field); data representation; expansions using a countable number of random variables; truncation and convergence results 
Abstract: No Abstract  
Christoph Schwab (ETH Zürich)  Lecture 6. The infinite dimensional case 
Abstract: We review representation results of the random solutions by socalled "generalized polynomial chaos" (gpc) expansions in countably many variables. We present recent mathematical results on regularity of such solutions as well as computational approaches for the adaptive numerical Galerkin and Collocation approximations of the infinite dimensional parametric, deterministic solution. A key principle are new sparsity estimates of gpc expansions of the parametric solution. We present such estimates for elliptic, parabolic and hyperbolic problems with random coefficients, as well as eigenvalue problems. We compare the possible convergence rates with the best convergence results on Monte Carlo Finite Element Methods (MCFEM) and on MLMCFEM. 

Guglielmo Scovazzi (Sandia National Laboratories)  On the need for uncertainty quantification in hyperbolic PDE applications at Sandia National Laboratories 
Abstract: A number of applications of interest at Sandia National Laboratories involve hyperbolic PDEs, and ultimately require uncertainty quantification methods. I will describe in general the nature of these applications and focus in particular on algorithms for shock hydrodynamics and transient dynamics problems based on tetrahedral finite elements. I will also be discussing perspectives on using this computational framework for complexgeometry fluidstructure interaction problems, in combination with mesh adaptation, optimization, and uncertainty quantification.  
Suvrajeet Sen (Ohio State University)  Multiscale stochastic optimization with applications in energy systems planning 
Abstract: Decision related to energy and environment are closely intertwined, and making choices based on only one of these factors has the potential to shortchange the other. However integrated models of these systems lead to ultra large scale systems which must be approximated at different levels of granularity. In particular, uncertainties themselves need to be modeled using alternate representations. We describe multiscale stochastic optimization models in which dynamic programming (or approximate DP) represent certain classes of decisions (e.g. control), where as stochastic programming is used for other classes of decisions (e.g. strategy). Multistage stochastic decomposition (a Monte Carlobased SP method) will play an important role in making it possible to integrate DP and SP.  
Alexander Shapiro (Georgia Institute of Technology)  Monte Carlo sampling techniques for solving stochastic and large scale deterministic optimization problems 
Abstract: The traditional approach to solving stochastic programming problems is based on construction scenarios representing a discretization of the underline (true) stochastic data process. Consequently, computational complexity of the obtained optimization problem is determined by the number of generated scenarios. Unfortunately the number of scenarios needed to approximate the "true" distribution of the data process grows exponentially both with increase of the number of random parameters and number of stages. A way of dealing with this explosion of the number of scenarios is to use randomization approaches based on Monte Carlo sampling techniques. In this talk we discuss theoretical and computational aspects of Monte Carlo sampling based approaches to solving two and multistage stochastic programming problems. Moreover, certain classes of deterministic problems can be formulated in terms of expected values and consequently randomization techniques can be applied to solve such large scale optimization problems. In particular, we discuss two competing approaches: the Sample Average Approximation (SAA) method and Stochastic Approximation (SA) type algorithms.  
Jie Shen (Purdue University)  Fast spectralGalerkin methods: from one dimension to high dimension 
Abstract: I shall talk about how to design fast spectralGalerkin algorithms for some prototypical partial differential equations. We shall start with algorithms in one dimension, then using a tensor product approach for two and three dimensions, and hyperbolic cross/spectral sparse grid for higher dimensional problems.  
Ian H. Sloan (University of New South Wales)  Porous flow as a high dimensional challenge 
Abstract: The problem of flow through a porous medium, with the permeability treated as a Gaussian random field, can be thought of as a highdimensional problem: the dimensionality might be the number of terms in a truncated KarhunenLoève expansion; or (as we prefer) the number of points in a discrete sampling of the porous medium. In this paper, describing recent joint work with F Kuo, I Graham, D. Nuyens and R Scheichl, we explore the use of quasiMonte Carlo methods to study various expected values of the flow through the medium, and to compare the results with the Monte Carlo method. The problem is computationally difficult if the permeability changes markedly from point to point, but the numerical results (obtained by evaluating integrals with as many as one million dimensions) are encouraging.  
Florian Steinke (Siemens)  Efficient uncertainty quantification for experiment design in sparse Bayesian models 
Abstract: We demonstrate how to perform experiment design for linear models with sparsity prior. Unlike maximum likelihood estimation, experiment design requires exact quantification of the estimation uncertainty and how this uncertainty would change given likely measurements. We employ a novel variant of the expectation propagation algorithm to approximate the posterior of the sparse linear model accurately and efficiently. The resulting experimental design method is motivated by and tested on the task of identifying gene regulatory networks with few experiments. The proposed method is one of the first to solve this problem in a statistically sound and efficient manner. In a realistic simulation study, it outperforms the only previous competitor significantly.  
Peter Tang (D. E. Shaw Research)  A Novel approach to tight bounds and statistical information of rounding errors 
Abstract: Obtaining tight bounds on rounding errors has been so specialized and laborintensive a task that it is seldom carried out during normal engineering practice in industry. It turns out that for absolute error analysis related to fixed point arithmetic, an automatic method can be devised for computation of linear transform. This method, implemented as a software tool, allows practicing engineers to obtain tight bounds as well as a vast amount of statistical information on forward rounding errors. The method consists of modeling the rounding error process in a way that allows mechanical computation on its propagation. When this model and propagation computation is implemented with objects and overloading in an object oriented manner, engineers can obtain detailed error information by means of algorithm implementation, not by actually carrying out error analysis. In this talk we will describe this method and illustrate its application on the very important Fast Fourier Transform.  
Raul F. Tempone (King Abdullah University of Science & Technology)  Lecture 2. Mathematical problems parametrized by a finite number of input random variables (finite dimensional case). Perturbation techniques and second order moment analysis. Sampling methods: Monte Carlo and variants; convergence analysis 
Abstract: No Abstract  
Raul F. Tempone (King Abdullah University of Science & Technology)  Lecture 3. Approximation of functions using polynomial or piecewise polynomial functions either by projection or interpolation. Stochastic Galerkin method (SGM): derivation; algorithmic aspects; preconditioning of the global system. Stochastic Collocation Method (SCM): collocation on tensor grids; sparse grid approximation; construction of generalized sparse grids 
Abstract: No Abstract  
Raul F. Tempone (King Abdullah University of Science & Technology)  Lecture 4. Elliptic equations with random input parameters: regularity results; convergence analysis for Galerkin and Collocation approximations. Anisotropic approximations 
Abstract: No Abstract  
Raul F. Tempone (King Abdullah University of Science & Technology)  Lecture 5. Numerical examples, numerical comparison of SGM and SCM. Adaptive approximation 
Abstract: No Abstract  
JeanPaul Watson (Sandia National Laboratories), David L. Woodruff (University of California, Davis)  Progressive hedging for multistage stochastic optimization problems 
Abstract: Although stochastic programming is a powerful tool for modeling decisionmaking under uncertainty, various impediments have historically prevented its widespread use. One key factor involves the ability of nonexperts to easily express stochastic programming problems, ideally building on a likely existing deterministic model expressed through an algebraic modeling language. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixedinteger, multistage case. Intricate and configurable (and often parallel) decomposition strategies are frequently required to achieve tractable runtimes. We simultaneously address both of these factors in our PySP software package, which is part of the COINOR Coopr opensource Python project for optimization. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in the Pyomo opensource algebraic modeling language. Given these two models, PySP provides two general paths for solution of the corresponding stochastic program. The first alternative involves writing the extensive form and invoking a standard deterministic mixedinteger solver. For more complex stochastic programs, we provide an implementation of Rockafellar and Wets' Progressive Hedging algorithm. Our particular focus is on the use of Progressive Hedging as an effective heuristic for approximating general multistage, mixedinteger stochastic programs. By leveraging the combination of a highlevel programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations on serial and parallel computers. PySP has been used by a number of research groups, including our own, to rapidly prototype and solve large and difficult stochastic programming problems.  
JeanPaul Watson (Sandia National Laboratories), David L. Woodruff (University of California, Davis)  PySP: Stochastic programming in Python 
Abstract: Real optimization problems have data that is uncertain and require the ability to update decisions as new information becomes available. Our poster describes open source modeling and solver software for multistage optimization with uncertain data, known as PySP (Python Stochastic Programming). We leverage a Python based software library called Coopr, developed at Sandia National Laboratories, to provide a full mixed integer modeling environment, which we have extended to allow for the description of multistage problems with data uncertainty. Users can write out the problem to be sent in its entirety to a variety of solvers or they can invoke the builtin Progressive Hedging solver that supports largescale parallelism. The Progressive Hedging solver is fully customizable, such that users can leverage problemspecific information to accelerate solution times.  
JeanPaul Watson (Sandia National Laboratories), David L. Woodruff (University of California, Davis)  Pyomo: An opensource tool for modeling and solving mathematical programs 
Abstract: We describe the Python Optimization Modeling Objects (Pyomo) software package. Pyomo supports the definition and solution of mathematical programming optimization applications using the Python scripting language. Python is a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo can be used to concisely represent mixedinteger linear and nonlinear programming (MILP) models for largescale, realworld problems that involve thousands of constraints and variables. Further, Pyomo includes a flexible framework for applying optimizers to analyze these models. Pyomo is distributed with a flexible opensource license (and is part of IBM’s COINOR initiative), which facilitates its use by both academic and commercial users.  
JeanPaul Watson (Sandia National Laboratories)  A stochastic programming groundwater remediation — flow/transport through porous media 
Abstract: No Abstract  
Roger J.B. Wets (University of California, Davis)  A brief review of variational analysis 
Abstract: Functions and their epigraphs, convexity and semicontinuity. Set convergence and epigraphical limits. Variational geometry, subgradients and subdifferential calculus.  
Roger J.B. Wets (University of California, Davis)  Random sets 
Abstract: Definition and properties of random sets, selections. The distribution function (∼ Choquet capacity) of a random set and convergence in distribution. The expectation of a random set and the law of large numbers for random sets. SAA (Sample. Average Approximations) of random sets. Application to stochastic variational inequalities and related variational problems.  
Roger J.B. Wets (University of California, Davis)  Random lsc functions and expectation functionals 
Abstract: Definition of random lsc (lower semicontinuous) functions and calculus. Stochastic processes with lsc paths. Properties of expectation functionals. Almost sure convergence and convergence in distribution (epigraphical sense). The Ergodic Theorem for random lsc functions and its applications: sampled variational problems, approximation, statistical estimation and homogenization.  
Roger J.B. Wets (University of California, Davis)  Introduction to the calculus of expectation functionals 
Abstract: Decomposable spaces. Fatou’s lemma for random set and random lsc functions. Interchange of minimization and (conditional) expectation. Subdifferentiation of expectation functionals. Martingale integrands and application to financial valuation.  
Karen E. Willcox (Massachusetts Institute of Technology)  Model reduction for uncertainty quantification and optimization under uncertainty of largescale complex systems 
Abstract: Uncertainty quantification approaches are generally computationally intractable for largescale complex systems. The discretized forward models describing such systems typically are of very high dimension and are expensive to solve. The computational resources required for uncertainty quantification therefore quickly become prohibitive. Model reduction can address this challenge by producing loworder approximate models that retain the essential system dynamics but that are fast to solve. This talk will discuss formulations of model reduction problems for applications in uncertainty quantification. Key challenges include systems with input parameter spaces of very high dimension (infinitedimensional parameters in some cases), and accounting for the statistical properties of interest in the system outputs. We demonstrate the use of reduced models for uncertainty propagation, solution of statistical inverse problems, and optimization under uncertainty for systems governed by partial differential equations. Our methods use state approximations through the proper orthogonal decomposition, reductions in parameter dimensionality through parameter basis approximations, and the empirical interpolation method for efficient evaluation of nonlinear terms. 

Ragnar Winther (University of Oslo)  Introduction to finite element exterior calculus 
Abstract: The purpose of this tutorial is to give an introduction to finite element exterior calculus, targeted to an audience which is reasonably familiar with topics like elliptic partial differential equations, Sobolev spaces, and finite element methods. We will first give a brief review of some of the fundamental concepts of exterior calculus, such as interior and exterior products, pullbacks, the Hodge star operation, the exterior derivative, and Stokes' theorem. Then we will focus on some of the main building blocks of finite element exterior calculus. In particular, we will discuss piecewise polynomial spaces of differential forms, degress of freedom, and the construction of bounded cochain projections. In addition, an abstract theory of Hilbert complexes will be presented, and we will explain how this relates to to the stability theory for approximations of the Hodge Laplacian.  
Yayun Zhou (Siemens)  Tool path planning with dual spherical spline 
Abstract: The novel tool path planning approach is proposed based on the offset theory and the kinematic ruled surface approximation. The designed blade surface is represented as a flank milling tool path with a cylindrical cutter in CNC machining. The drive surface is a ruled surface, which is denoted as a dual spherical spline. It is derived by kinematically approximating the offset surface of the original design as a ruled surface. This approach integrates the manufacture requirements into the design phase, which reduces the developing cycle time and the manufacturing cost.  
Erik von Schwerin (King Abdullah University of Science & Technology)  Adaptive multi level Monte Carlo simulation 
Abstract: Microscopic models in physical sciences are often stochastic; for
example time evolutions modelled by stochastic ordinary differential
equations (SDEs). The numerical methods for approximating expected
values of functions depending on the solution of Ito SDEs were
significantly improved when the multilevel Forward Euler Monte Carlo
method was introduced in [1]. This poster presents a generalization of
the method in [1]. The work [1] proposed and analysed Multilevel Monte
Carlo method based on a hierarchy of uniform time discretizations and
control variates to reduce the computational effort required by a
standard, single level, Forward Euler Monte Carlo method. The present
work introduces and analyses an adaptive hierarchy of non uniform time
discretizations, generated by adaptive algorithms introduced in
[3,2]. These adaptive algorithms apply either deterministic time steps
or stochastic time steps and are based on a posteriori error expansions
first developed in [4]. Under sufficient regularity conditions, both
our analysis and numerical results, which include one case with
singular drift and one with stopped diffusion, exhibit savings in the
computational cost to achieve an accuracy of O(TOL), from O(TOL^{3}) to
O(TOL^{1} log (TOL))^{2}. This poster presents joint work with H. Hoel, A. Szepessy, and R. Tempone. References: [1] Michael B. Giles. Multilevel Monte Carlo path simulation. Oper. Res., 56(3):607617, 2008. [2] KyoungSook Moon, Anders Szepessy, Raul Tempone, and Georgios E. Zouraris. Convergence rates for adaptive weak approximation of stochastic diffential equations. Stoch. Anal. Appl., 23(3):511558, 2005. [3] KyoungSook Moon, Erik von Schwerin, Anders Szepessy, and Raul Tempone. An adaptive algorithm for ordinary, stochastic and partial differential equations. In Recent advances in adaptive computation, volume 383 of Contemp. Math., pages 325343. Amer. Math. Soc., Providence, RI, 2005. [4] Anders Szepessy, Raul Tempone, and Georgios E. Zouraris. Adaptive weak approximation of stochastic differential equations. Comm. Pure Appl. Math., 54(10):11691214, 2001. 
Yasaman Adibi  University of Minnesota  10/16/2010  10/17/2010 
Slimane Adjerid  Rensselaer Polytechnic Institute  10/31/2010  11/5/2010 
Alexander Alekseenko  California State University  9/1/2010  12/31/2010 
Mohamed Almekkawy  University of Minnesota  10/16/2010  10/17/2010 
Roman Andreev  ETH Zürich  10/17/2010  10/23/2010 
Roman Andreev  ETH Zürich  10/31/2010  11/6/2010 
Aleksandr Yakovlevitch Aravkin  University of Washington  10/17/2010  10/22/2010 
Todd Arbogast  University of Texas at Austin  10/31/2010  11/7/2010 
Douglas N. Arnold  University of Minnesota  9/1/2010  6/30/2011 
Florian Augustin  TU München  10/16/2010  10/23/2010 
Gerard Michel Awanou  Northern Illinois University  9/1/2010  6/10/2011 
Blanca Ayuso de Dios  Centre de Recerca Matemàtica  10/30/2010  12/18/2010 
Constantin Bacuta  University of Delaware  10/31/2010  11/7/2010 
Nusret Balci  University of Minnesota  9/1/2009  8/31/2011 
Uday Banerjee  Syracuse University  9/1/2010  12/4/2010 
Andrew T. Barker  Louisiana State University  10/31/2010  11/6/2010 
Timothy J. Barth  NASA Ames Research Center  10/17/2010  10/22/2010 
Peter W. Bates  Michigan State University  10/10/2010  10/11/2010 
Yuri Bazilevs  University of California, San Diego  10/31/2010  11/5/2010 
Bradley M. Bell  University of Washington  10/17/2010  10/22/2010 
Naoufel Ben Abdallah  Université de Toulouse III (Paul Sabatier)  10/31/2010  11/5/2010 
Christine Bernardi  Université de Paris VI (Pierre et Marie Curie)  10/31/2010  11/5/2010 
Pavel B. Bochev  Sandia National Laboratories  10/30/2010  11/7/2010 
Daniele Boffi  Università di Pavia  10/30/2010  11/7/2010 
Francesca Bonizzoni  Politecnico di Milano  10/15/2010  11/10/2010 
Susanne C. Brenner  Louisiana State University  9/1/2010  6/10/2011 
Russell Brown  University of Kentucky  10/11/2010  10/21/2010 
James V. Burke  University of Washington  10/17/2010  10/22/2010 
Daniela Calvetti  Case Western Reserve University  10/17/2010  10/20/2010 
Claudio Canuto  Politecnico di Torino  10/17/2010  11/7/2010 
David Buck Carlson  Wartburg College  10/30/2010  10/30/2010 
Julio Enrique Castrillon Candas  King Abdullah University of Science & Technology  10/15/2010  10/22/2010 
Fatih Celiker  Wayne State University  9/1/2010  12/31/2010 
Aycil Cesmelioglu  University of Minnesota  9/30/2010  8/30/2011 
Chi Hin Chan  University of Minnesota  9/1/2009  8/31/2011 
Feng Chen  Purdue University  10/30/2010  11/4/2010 
Qiang Chen  University of Delaware  10/31/2010  11/6/2010 
Yanlai Chen  University of Massachusetts, Dartmouth  10/31/2010  11/7/2010 
Zhiming Chen  Chinese Academy of Sciences  10/31/2010  11/7/2010 
Yingda Cheng  Brown University  10/31/2010  11/7/2010 
Alexey Chernov  Rheinische FriedrichWilhelmsUniversität Bonn  10/15/2010  10/23/2010 
David Chock  NONE  10/9/2010  10/11/2010 
ShueSum Chow  Brigham Young University  10/31/2010  11/7/2010 
Jonathan Claridge  University of Washington  10/4/2010  10/8/2010 
Bernardo Cockburn  University of Minnesota  9/1/2010  6/30/2011 
Robert Crone  Seagate Technology  10/18/2010  10/22/2010 
Jintao Cui  University of Minnesota  8/31/2010  8/30/2011 
Qing Cui  University of Minnesota  1/1/2010  12/1/2010 
Clint Dawson  University of Texas at Austin  10/31/2010  11/4/2010 
Leszek Feliks Demkowicz  University of Texas at Austin  10/31/2010  11/7/2010 
Alireza Doostan  University of Colorado  10/18/2010  10/21/2010 
Tobin A. Driscoll  University of Delaware  8/26/2010  12/20/2010 
Mohammad Ebtehaj  University of Minnesota  10/16/2010  10/17/2010 
Yalchin Efendiev  Texas A & M University  10/18/2010  10/23/2010 
Amr S. ElBakry  ExxonMobil  10/15/2010  10/22/2010 
Hallie M Elich  University of Minnesota  10/16/2010  10/17/2010 
Randy H. Ewoldt  University of Minnesota  9/1/2009  8/31/2011 
Richard S Falk  Rutgers University  9/19/2010  12/18/2010 
Yueyue Fan  University of California, Davis  10/17/2010  10/22/2010 
Xiaobing Henry Feng  University of Tennessee  10/29/2010  12/15/2010 
Oscar E. Fernandez  University of Minnesota  8/31/2010  8/30/2011 
Michael C. Ferris  University of Wisconsin  10/17/2010  10/22/2010 
Don Ford  U.S. Department of Agriculture (USDA)  10/17/2010  10/23/2010 
Juan Carlos Galvis  Texas A & M University  10/18/2010  10/23/2010 
Baskar Ganapathysubramanian  Iowa State University  10/17/2010  10/22/2010 
Carlos Andres GaravitoGarzon  University of Minnesota  10/30/2010  10/31/2010 
Lucia Gastaldi  Università di Brescia  10/30/2010  11/7/2010 
Nikolaos Gatsis  University of Minnesota  10/16/2010  10/17/2010 
Gaurav Gaurav  University of Minnesota  10/16/2010  10/22/2010 
Joscha Gedicke  HumboldtUniversität  10/30/2010  12/4/2010 
Luca Gerardo Giorda  Emory University  10/15/2010  10/18/2010 
Marc Iwan Gerritsma  Technische Universiteit te Delft  10/30/2010  11/6/2010 
Omar Ghattas  University of Texas at Austin  10/17/2010  10/22/2010 
Robert Ghrist  University of Pennsylvania  10/9/2010  10/11/2010 
Anna Gilbert  University of Michigan  10/9/2010  10/11/2010 
Andrew Kruse Gillette  University of Texas at Austin  10/31/2010  11/6/2010 
Claude Jeffrey Gittelson  ETH  10/17/2010  10/23/2010 
Jay Gopalakrishnan  University of Florida  9/1/2010  6/30/2011 
Genetha Anne Gray  Sandia National Laboratories  10/17/2010  10/21/2010 
Shiyuan Gu  Louisiana State University  9/1/2010  6/30/2011 
Helmut Harbrecht  Universität Stuttgart  10/16/2010  10/23/2010 
Xiaoming He  Missouri University of Science and Technology  10/19/2010  10/22/2010 
Christian Louis Hess  Université de ParisDauphine  10/16/2010  10/22/2010 
Jan S. Hesthaven  Brown University  10/30/2010  11/6/2010 
Robert L. Higdon  Oregon State University  10/31/2010  11/6/2010 
Ronald H.W. Hoppe  University of Houston  9/6/2010  12/20/2010 
Raya Horesh  University of Minnesota  10/15/2010  11/6/2010 
Mary Ann Horn  National Science Foundation  10/9/2010  10/12/2010 
Thomas Yizhao Hou  California Institute of Technology  10/9/2010  10/11/2010 
Thomas Yizhao Hou  California Institute of Technology  10/30/2010  11/4/2010 
Jason Howell  Clarkson University  10/31/2010  11/7/2010 
James W Howse  Los Alamos National Laboratory  10/17/2010  10/23/2010 
Yulia Hristova  University of Minnesota  9/1/2010  8/31/2011 
Lili Hu  Georgia Institute of Technology  10/31/2010  11/5/2010 
Nitin Jain  University of Minnesota  10/18/2010  10/22/2010 
Lijian Jiang  Los Alamos National Laboratory  10/15/2010  10/23/2010 
Alejandro Rene Jofre  University of Chile  10/15/2010  10/20/2010 
Sunnie Joshi  Texas A & M University  10/30/2010  11/5/2010 
Mihailo Jovanovic  University of Minnesota  10/16/2010  10/17/2010 
Lili Ju  University of South Carolina  10/31/2010  11/4/2010 
Evangelia Kalligiannaki  University of Tennessee  10/15/2010  10/27/2010 
Myungjoo Kang  Seoul National University  10/31/2010  11/5/2010 
Guido Kanschat  Texas A & M University  9/6/2010  12/20/2010 
ChiuYen Kao  Ohio State University  9/1/2010  12/20/2010 
Jesper Karlsson  King Abdullah University of Science & Technology  10/14/2010  10/22/2010 
Markos A. Katsoulakis  University of Massachusetts  10/17/2010  10/28/2010 
Markus Keel  University of Minnesota  7/21/2008  6/30/2011 
Vahid Keshavarzzadeh  University of Southern California  10/15/2010  10/22/2010 
Abdul Qayyum Masud Khaliq  Middle Tennessee State University  10/17/2010  10/23/2010 
Alan King  IBM  10/17/2010  10/20/2010 
Pawel Konieczny  University of Minnesota  9/1/2009  8/31/2011 
Kristina Kraakmo  University of Central Florida  10/30/2010  11/3/2010 
Angela Kunoth  Universität Paderborn  10/31/2010  11/7/2010 
Diane Lambert  Google Inc.  10/9/2010  10/11/2010 
Ilya Lashuk  Lawrence Livermore National Laboratory  10/29/2010  11/5/2010 
Olivier Pierre Le Maître  Centre National de la Recherche Scientifique (CNRS)  10/17/2010  10/23/2010 
Gilad Lerman  University of Minnesota  9/1/2010  6/30/2011 
Randall J. Leveque  University of Washington  9/12/2010  10/16/2010 
Dmitriy Leykekhman  University of Connecticut  10/31/2010  11/7/2010 
Chaodi Li  3M  10/18/2010  10/22/2010 
Fengyan Li  Rensselaer Polytechnic Institute  9/1/2010  12/20/2010 
Hengguang Li  University of Minnesota  8/16/2010  8/15/2011 
Lizao Li  University of Minnesota  10/16/2010  10/17/2010 
Peng Li  University of Minnesota  10/16/2010  10/17/2010 
Wenbo Li  University of Delaware  10/16/2010  10/18/2010 
Yan Li  University of Minnesota  10/30/2010  11/6/2010 
Zhilin Li  North Carolina State University  10/31/2010  11/5/2010 
Hyeona Lim  Mississippi State University  10/30/2010  11/4/2010 
Fu Lin  University of Minnesota  10/16/2010  10/17/2010 
Guang Lin  Pacific Northwest National Laboratory  10/15/2010  10/22/2010 
Guang Lin  Pacific Northwest National Laboratory  10/29/2010  11/3/2010 
Zhi (George) Lin  University of Minnesota  9/1/2009  8/31/2011 
Robert P. Lipton  Louisiana State University  10/17/2010  10/22/2010 
Hailiang Liu  Iowa State University  10/31/2010  11/5/2010 
Jiangguo (James) Liu  Colorado State University  10/31/2010  11/7/2010 
Vanessa LopezMarrero  IBM  10/17/2010  10/23/2010 
Alexei Lozinski  Université de Toulouse III (Paul Sabatier)  10/31/2010  11/6/2010 
Shu Lu  University of North Carolina  10/17/2010  10/22/2010 
Ying Lu  University of Minnesota  10/16/2010  10/17/2010 
Laura Lurati  Boeing  10/17/2010  10/22/2010 
Mitchell Luskin  University of Minnesota  9/1/2010  6/30/2011 
Lina Ma  Purdue University  10/31/2010  11/6/2010 
Scott MacLachlan  Tufts University  10/24/2010  12/3/2010 
Niall Madden  National University of Ireland, Galway  10/18/2010  12/10/2010 
Andrew J. Majda  New York University  10/17/2010  10/20/2010 
Kara Lee Maki  University of Minnesota  9/1/2009  8/31/2011 
Kyle Mandli  University of Washington  10/4/2010  10/16/2010 
Yi Mao  University of Tennessee  10/17/2010  10/22/2010 
Yu (David) Mao  University of Minnesota  8/31/2010  8/30/2011 
Dionisios Margetis  University of Maryland  10/17/2010  10/21/2010 
Maider Judith MarinMcGee  University of Puerto Rico  10/29/2010  11/6/2010 
Osama Masoud  ViTAL Images, Inc.  10/8/2010  10/8/2010 
Jens Markus Melenk  Technische Universität Wien  10/30/2010  11/7/2010 
Giovanni Migliorati  Politecnico di Milano  10/14/2010  10/24/2010 
Laurie E. Miller  University of Minnesota  10/16/2010  10/17/2010 
Irina Mitrea  University of Minnesota  8/16/2010  6/14/2011 
Dimitrios Mitsotakis  University of Minnesota  10/27/2010  8/31/2011 
Rashad Moarref  University of Minnesota  10/16/2010  10/17/2010 
Peter Monk  University of Delaware  9/8/2010  12/10/2010 
Brian Edward Moore  University of Central Florida  10/31/2010  11/3/2010 
David Morton  University of Texas at Austin  10/17/2010  10/22/2010 
Robert D. Moser  University of Texas at Austin  10/19/2010  10/22/2010 
Eva Mossberg  Karlstad University  10/17/2010  10/22/2010 
Magnus Mossberg  Karlstad University  10/17/2010  10/22/2010 
Zhe Nan  Louisiana State University  10/30/2010  11/7/2010 
Michael Joseph Neilan  Louisiana State University  10/15/2010  10/22/2010 
Michael Joseph Neilan  Louisiana State University  10/29/2010  11/7/2010 
NgocCuong Nguyen  Massachusetts Institute of Technology  10/31/2010  11/5/2010 
Fabio Nobile  Politecnico di Milano  10/17/2010  10/24/2010 
Ricardo H. Nochetto  University of Maryland  9/13/2010  12/17/2010 
Alexandra Ortan  University of Minnesota  9/16/2010  6/15/2011 
Cecilia OrtizDuenas  University of Minnesota  9/1/2009  8/31/2011 
MiaoJung Yvonne Ou  Oak Ridge National Laboratory  8/30/2010  12/10/2010 
Jeffrey Ovall  University of Kentucky  10/31/2010  11/7/2010 
Mattia Padulo  National Aeronautics and Space Administration (NASA)  10/15/2010  10/23/2010 
EunHee Park  Louisiana State University  10/30/2010  11/7/2010 
Teemu Pennanen  Helsinki University of Technology  10/16/2010  10/23/2010 
Jaime Peraire  Massachusetts Institute of Technology  10/31/2010  11/5/2010 
Ilaria Perugia  Università di Pavia  10/30/2010  11/6/2010 
Malgorzata Peszynska  Oregon State University  10/17/2010  10/22/2010 
Arlie O. Petters  Duke University  10/9/2010  10/11/2010 
Per Pettersson  Stanford University  10/15/2010  10/17/2010 
Andy Philpott  University of Auckland  10/17/2010  10/23/2010 
Petr Plechac  University of Tennessee  9/1/2010  12/10/2010 
Catherine E. Powell  University of Manchester  10/17/2010  10/23/2010 
Serge Prudhomme  University of Texas at Austin  10/17/2010  10/22/2010 
Jingmei Qiu  Colorado School of Mines  10/31/2010  11/3/2010 
Weifeng (Frederick) Qiu  University of Minnesota  8/31/2010  8/30/2011 
Vincent QuennevilleBelair  University of Minnesota  9/16/2010  6/15/2011 
Rachel Quinlan  National University of Ireland, Galway  10/18/2010  12/10/2010 
Dana Randall  Georgia Institute of Technology  10/10/2010  10/11/2010 
Darsh Priya Ranjan  University of California, Berkeley  10/29/2010  11/6/2010 
Armin Reiser  Louisiana State University  9/1/2010  12/15/2010 
Fernando Reitich  University of Minnesota  9/1/2010  6/30/2011 
Donald Richards  Pennsylvania State University  10/9/2010  10/11/2010 
Joyce Cristina Rigelo  University of Wyoming  10/17/2010  10/23/2010 
Irina Rish  IBM  10/28/2010  10/30/2010 
Stephen Michael Robinson  University of Wisconsin  10/17/2010  10/22/2010 
R. Tyrrell Rockafellar  University of Washington  10/13/2010  10/26/2010 
Werner Römisch  HumboldtUniversität  10/16/2010  10/22/2010 
Gianluigi Rozza  École Polytechnique Fédérale de Lausanne (EPFL)  10/30/2010  11/6/2010 
Mattias Sandberg  Royal Institute of Technology (KTH)  10/15/2010  10/23/2010 
Giancarlo Sangalli  Università di Pavia  10/31/2010  11/6/2010 
Fadil Santosa  University of Minnesota  7/1/2008  6/30/2011 
FranciscoJavier Sayas  University of Delaware  10/28/2010  11/7/2010 
Reinhold Schneider  TU Berlin  10/30/2010  11/6/2010 
Joachim Schöberl  Technische Universität Wien  10/31/2010  11/6/2010 
Dominik M. Schoetzau  University of British Columbia  10/30/2010  11/7/2010 
Christoph Schwab  ETH Zürich  10/15/2010  10/23/2010 
Christoph Schwab  ETH Zürich  10/31/2010  11/7/2010 
Marc Alexander Schweitzer  Rheinische FriedrichWilhelmsUniversität Bonn  10/31/2010  11/7/2010 
Guglielmo Scovazzi  Sandia National Laboratories  10/15/2010  10/22/2010 
Guglielmo Scovazzi  Sandia National Laboratories  10/29/2010  11/5/2010 
Suvrajeet Sen  Ohio State University  10/17/2010  10/22/2010 
Shuanglin Shao  University of Minnesota  9/1/2009  8/31/2011 
Alexander Shapiro  Georgia Institute of Technology  10/17/2010  10/21/2010 
David H. Sharp  Los Alamos National Laboratory  10/8/2010  10/12/2010 
Mikhail Shashkov  Los Alamos National Laboratory  10/31/2010  11/5/2010 
Jie Shen  Purdue University  10/30/2010  11/6/2010 
Luwei Shen  University of Minnesota  10/20/2010  10/22/2010 
Ke Shi  University of Minnesota  10/30/2010  11/6/2010 
ChiWang Shu  Brown University  10/31/2010  11/6/2010 
John Singler  Missouri University of Science and Technology  10/19/2010  10/22/2010 
Ian H. Sloan  University of New South Wales  10/16/2010  10/23/2010 
Erkki Somersalo  Case Western Reserve University  10/18/2010  10/21/2010 
Panagiotis E. Souganidis  University of Chicago  10/10/2010  10/11/2010 
Orhan Soykan  Medtronic  10/16/2010  10/17/2010 
Florian Steinke  Siemens  10/16/2010  10/22/2010 
Ari Stern  University of California, San Diego  10/31/2010  11/6/2010 
Rob Stevenson  Universiteit van Amsterdam  10/30/2010  11/6/2010 
Panagiotis Stinis  University of Minnesota  9/1/2010  6/30/2011 
Jiguang Sun  Delaware State University  10/31/2010  11/7/2010 
Tong Sun  Bowling Green State University  10/30/2010  11/7/2010 
Yi Sun  Statistical and Applied Mathematical Sciences Institute (SAMSI)  10/31/2010  11/4/2010 
Liyeng Sung  Louisiana State University  9/1/2010  6/15/2011 
Vladimir Sverak  University of Minnesota  10/10/2010  10/11/2010 
Daniil Svyatskiy  Los Alamos National Laboratory  10/15/2010  10/20/2010 
Lorenzo Tamellini  Politecnico di Milano  10/15/2010  10/23/2010 
Peter Tang  D. E. Shaw Research  10/14/2010  10/15/2010 
Nicolae Tarfulea  Purdue University, Calumet  9/1/2010  6/15/2011 
Daniel M. Tartakovsky  University of California, San Diego  10/17/2010  10/20/2010 
Raul F. Tempone  King Abdullah University of Science & Technology  10/14/2010  10/23/2010 
Ramakrishna Tipireddy  University of Southern California  10/15/2010  10/22/2010 
Charles Tong  Lawrence Livermore National Laboratory  10/17/2010  10/22/2010 
Dimitar Trenev  University of Minnesota  9/1/2009  8/31/2011 
Catalin Turc  Case Western Reserve University  10/31/2010  11/5/2010 
Karsten Urban  Universität Ulm  10/18/2010  10/22/2010 
Brian Vachta  Wartburg College  10/30/2010  10/31/2010 
Panayot S Vassilevski  Lawrence Livermore National Laboratory  10/31/2010  11/5/2010 
Diane E. Vaughan  Los Alamos National Laboratory  10/17/2010  10/20/2010 
Chad N Vidden  Iowa State University  10/31/2010  11/5/2010 
Peter Edward Vincent  Stanford University  10/31/2010  11/6/2010 
Erik von Schwerin  King Abdullah University of Science & Technology  10/14/2010  10/25/2010 
Shawn W. Walker  Louisiana State University  10/30/2010  11/6/2010 
Xiaoliang Wan  Louisiana State University  10/17/2010  10/23/2010 
Wei Wang  Florida International University  10/31/2010  11/7/2010 
JeanPaul Watson  Sandia National Laboratories  10/17/2010  10/22/2010 
Roger J.B. Wets  University of California, Davis  10/13/2010  10/26/2010 
Karen E. Willcox  Massachusetts Institute of Technology  10/18/2010  10/22/2010 
Ragnar Winther  University of Oslo  10/17/2010  11/12/2010 
Barbara Wohlmuth  Technical University of Munich  10/30/2010  11/6/2010 
Steven F. Wojtkiewicz  University of Minnesota  10/18/2010  10/22/2010 
David L. Woodruff  University of California, Davis  10/17/2010  10/22/2010 
Han Wu  Minnesota State University  10/16/2010  10/17/2010 
Huifu Xu  University of Southampton  10/16/2010  10/22/2010 
Liwei Xu  Rensselaer Polytechnic Institute  10/31/2010  11/7/2010 
Guangri Xue  University of Texas at Austin  10/29/2010  11/7/2010 
Lingzhou Xue  University of Minnesota  10/16/2010  10/22/2010 
Sergey Borisovich Yakovlev  Rensselaer Polytechnic Institute  9/8/2010  12/15/2010 
Jue Yan  Iowa State University  10/31/2010  11/7/2010 
ChinAnn Yang  University of Minnesota  10/16/2010  10/17/2010 
Xingzhou Yang  Mississippi State University  10/29/2010  11/4/2010 
Shantia Yarahmadian  Mississippi State University  10/15/2010  10/17/2010 
Shantia Yarahmadian  Mississippi State University  10/29/2010  10/31/2010 
Xiu Ye  University of Arkansas  10/31/2010  11/7/2010 
Feng Yi  University of Minnesota  10/16/2010  10/17/2010 
Hongxia Yin  Minnesota State University  10/16/2010  10/17/2010 
Haijun Yu  Purdue University  10/31/2010  11/6/2010 
Hui Yu  Iowa State University  10/31/2010  11/5/2010 
Chuan Zhang  University of Minnesota  10/16/2010  10/17/2010 
H. Michael Zhang  University of California, Davis  10/17/2010  10/22/2010 
Zhimin Zhang  Wayne State University  10/31/2010  11/7/2010 
Zhongqiang Zhang  Brown University  10/17/2010  10/23/2010 
Shan Zhao  University of Alabama  10/31/2010  11/2/2010 
Yayun Zhou  Siemens  10/16/2010  10/23/2010 
Hui Zou  University of Minnesota  10/18/2010  10/22/2010 