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.
2:30pm3:00pm  Coffee Break  Lind Hall 400 
8:00am8:30am  Registration and coffee  Lind Hall 400  SW6.24.11  
8:30am8:45pm  Welcome to the IMA  Fadil Santosa (University of Minnesota)  Lind Hall 305  SW6.24.11 
8:45am9:45am  Challenges In Uncertainty, Calibration, Validation and Predictability of Engineering Analysis Models  Liping Wang (General Electric)  Lind Hall 305  SW6.24.11 
9:45am10:45am  Scientific and statistical challenges to quantifying uncertainties in climate projections  Charles S. Jackson (University of Texas at Austin)  Lind Hall 305  SW6.24.11 
10:45am11:15am  Coffee break  Lind Hall 400  SW6.24.11  
11:15am12:15pm  GradientEnhanced Uncertainty Propagation  Mihai Anitescu (Argonne National Laboratory)  Lind Hall 305  SW6.24.11 
12:15pm1:45pm  Lunch  SW6.24.11  
1:45pm2:45pm  Multiple Model Inference: Calibration and Selection with Multiple Models  Laura Swiler (Sandia National Laboratories)  Lind Hall 305  SW6.24.11 
3:00pm4:00pm  Improved Quantification of Prediction Error for Kriging Response Surfaces  Donald R. Jones (General Motors)  Lind Hall 305  SW6.24.11 
4:00pm6:00pm  Reception and Poster Session Poster submissions welcome from all participants  Lind Hall 400  SW6.24.11  
Poster Algorithm Class ARODE  Florian Augustin (TU München)  
Poster Robust Design for Industrial Applications  Albert B. Gilg (Siemens) Utz Wever (Siemens)  
Poster  Scientific and statistical challenges to quantifying uncertainties in climate projections  Charles S. Jackson (University of Texas at Austin)  
Poster The Uncertainty Quantification Project at Lawrence Livermore National Laboratory: Sensitivities and Uncertainties of the Community Atmosphere Model  Gardar Johannesson (Lawrence Livermore National Laboratory)  
Poster  Error Reduction and Optimal Parameters Estimation in Convective Cloud Scheme in Climate Model  Guang Lin (Pacific Northwest National Laboratory)  
Poster Stochastic TwoStage Problems with Stochastic Dominance Constraint  María Gabriela Martínez López (Stevens Institute of Technology)  
Poster  Polynomial Chaos for Differential Algebraic Equations with Random Parameters  Roland Pulch (Bergische UniversitätGesamthochschule Wuppertal (BUGH))  
Poster An Information Theoretic Approach to Model Calibration and Validation using QUESO  Gabriel Alin Terejanu (University of Texas at Austin) 
All Day  Morning Session Chair: Roger Ghanem (University of Souther California)  
All Day  Morning Session Chair: Roger G. Ghanem (University of Southern California)  SW6.24.11  
8:30am9:00am  Coffee  Lind Hall 400  SW6.24.11  
9:00am10:00am  Scenario generation in stochastic programming with application to optimizing electricity portfolios under uncertainty  Werner Römisch (HumboldtUniversität)  Lind Hall 305  SW6.24.11 
10:00am11:00am  Uncertainty quantification of shock interactions with complex environments  George C. Papanicolaou (Stanford University)  Lind Hall 305  SW6.24.11 
11:00am12:00pm  Discussion Session  Lind Hall 305  SW6.24.11  
12:00pm1:00pm  Lunch  SW6.24.11  
1:00pm2:00pm  Mastering Impact of Uncertainties by Robust Design Optimization Techniques for TurboMachinery  Albert B. Gilg (Siemens)  Lind Hall 305  SW6.24.11 
2:00pm3:00pm  Efficient UQ algorithms for practical systems  Dongbin Xiu (Purdue University)  Lind Hall 305  SW6.24.11 
3:00pm3:15pm  Group Photo  SW6.24.11  
3:15pm3:30pm  Coffee break  Lind Hall 400  SW6.24.11  
3:30pm4:30pm  The Curse of Dimensionality, Model Validation, and UQ.  Roger G. Ghanem (University of Southern California)  Lind Hall 305  SW6.24.11 
4:30pm5:30pm  Discussion Session  Lind Hall 305  SW6.24.11  
6:00pm8:30pm  Social Hour at the Campus Club  Coffman Memorial Union  300 Washington Avenue SEMinneapolis MN 55455  SW6.24.11 
8:30am9:00am  Coffee  Lind Hall 400  SW6.24.11  
9:00am10:00am  Uncertainty Quantification and Optimization Under Uncertainty: Experience and Challenges  Andrew J. Booker (Boeing)  Lind Hall 305  SW6.24.11 
10:00am11:00am  Design For Variation at Pratt & Whitney  Grant Reinman (Pratt & Whitney)  Lind Hall 305  SW6.24.11 
11:00am12:00pm  Final Discussion Session  Lind Hall 305  SW6.24.11 
8:30am9:00am  Registration and coffee  Lind Hall 400  T6.5.11  
9:00am10:30am  Tutorial  Luis Tenorio (Colorado School of Mines)  Lind Hall 305  T6.5.11 
10:30am11:00am  Coffee break  Lind Hall 400  T6.5.11  
11:00am12:30pm  Tutorial (continued)  Luis Tenorio (Colorado School of Mines)  Lind Hall 305  T6.5.11 
12:30pm2:00pm  Lunch  T6.5.11  
2:00pm3:30pm  Tutorial  Youssef Marzouk (Massachusetts Institute of Technology)  Lind Hall 305  T6.5.11 
3:30pm4:00pm  Coffee break  Lind Hall 400  T6.5.11  
4:00pm5:30pm  Tutorial (continued)  Youssef Marzouk (Massachusetts Institute of Technology)  Lind Hall 305  T6.5.11 
All Day  Chairs: Omar Ghattas (University of Texas at Austin) and Karen E. Willcox (Massachusetts Institute of Technology)  W6.610.11  
9:00am9:30am  Registration and coffee  Keller Hall 3176  W6.610.11  
9:30am9:45am  Welcome to the IMA  Fadil Santosa (University of Minnesota)  Keller Hall 3180  W6.610.11 
9:45am10:30am  Introduction blitz by participants  Keller Hall 3180  W6.610.11  
10:30am11:00am  Coffee break  Keller Hall 3176  W6.610.11  
11:00am12:00pm  Workshop Introduction  Omar Ghattas (University of Texas at Austin) Karen E. Willcox (Massachusetts Institute of Technology)  Keller Hall 3180  W6.610.11 
12:00pm1:45pm  Lunch  W6.610.11  
1:45pm2:45pm  The best we can do with MCMC, and how to do better.  Colin Fox (University of Otago)  Keller Hall 3180  W6.610.11 
2:45pm3:00pm  Coffee break  Keller Hall 3176  W6.610.11  
3:00pm4:00pm  Confidence in Image Reconstruction  Dianne P. O'Leary (University of Maryland)  Keller Hall 3180  W6.610.11 
4:00pm4:15pm  Group photo  W6.610.11 
All Day  Chairs: Luis Tenorio (Colorado School of Mines) and Eldad Haber (University of British Columbia)  W6.610.11  
8:30am9:00am  Coffee  Keller Hall 3176  W6.610.11  
9:00am10:00am  Systemtheoretical aspects of oil and gas reservoir history matching  Jan Dirk Jansen (Delft University of Technology)  Keller Hall 3180  W6.610.11 
10:00am10:30am  Coffee break  Keller Hall 3176  W6.610.11  
10:30am11:30am  Spatial categorical inversion: Seismic inversion into lithology/fluid classes  Henning Omre (Norwegian University of Science and Technology (NTNU))  Keller Hall 3180  W6.610.11 
11:30am1:00pm  Lunch  W6.610.11  
1:00pm2:00pm  Ensemblebased methods: filters, smoothers and iteration  Dean S. Oliver (University of Bergen)  Keller Hall 3180  W6.610.11 
2:00pm2:30pm  Coffee break  Keller Hall 3176  W6.610.11  
2:30pm3:30pm  Ocean Uncertainty Prediction and nonGaussian Data Assimilation with Stochastic PDEs: ByeBye MonteCarlo?  Pierre FJ Lermusiaux (Massachusetts Institute of Technology)  Keller Hall 3180  W6.610.11 
3:30pm5:30pm  Reception and Poster Session Poster submissions welcome from all participants Instructions  Lind Hall 400  W6.610.11  
Poster Detecting small low emission radiating sources  Moritz Allmaras (Texas A & M University) Yulia Hristova (University of Minnesota)  
Poster  Scalable parallel algorithms for uncertainty quantification in high dimensional inverse problems  Tan BuiThanh (University of Texas at Austin)  
Poster Designing Optimal Spectral Filters for Inverse Problems  Julianne Chung (University of Maryland)  
Poster Bayesian Inference for Data Assimilation using LeastSquares Finite Element Methods  Richard Dwight (Delft University of Technology)  
Poster  Convergence of a greedy algorithm for highdimensional convex nonlinear problems  Virginie Ehrlacher (École des Ponts ParisTech)  
Poster Robust Design for Industrial Applications  Albert B. Gilg (Siemens) Utz Wever (Siemens)  
Poster  Sparsity reconstruction in electrical impedance tomography  Bangti Jin (Texas A & M University)  
Poster A Multiscale Learning Approach for History Matching  Hector Klie (ConocoPhillips)  
Poster Information Gain in Model Validation for Porous Media  Quan Long King Abdullah University of Science & Technology, University of Texas at Austin  
PosterA hybrid numerical method for the numerical solution of the Benjamin equation  Dimitrios Mitsotakis (University of Minnesota)  
Poster Modeling and Analysis of HIV Evolution and Therapy  Nicolae Tarfulea (Purdue University, Calumet) 
All Day  Chairs: Omar Ghattas (University of Texas at Austin) and Luis Tenorio (Colorado School of Mines)  W6.610.11  
8:30am9:00am  Coffee  Keller Hall 3176  W6.610.11  
9:00am10:00am  Design of simultaneous source  Eldad Haber (University of British Columbia)  Keller Hall 3180  W6.610.11 
10:00am10:30am  Coffee break  Keller Hall 3176  W6.610.11  
10:30am11:30am  Data Assimilation and Efficient Forward Modeling for Subsurface Flow  Louis J. Durlofsky (Stanford University)  Keller Hall 3180  W6.610.11 
11:30am1:00pm  Lunch  W6.610.11  
1:00pm2:00pm  Bayesian approaches for combining computational model output and physical observations  David Higdon (Los Alamos National Laboratory)  Keller Hall 3180  W6.610.11 
2:00pm2:30pm  Coffee break  Keller Hall 3176  W6.610.11  
2:30pm3:30pm  A mapbased approach to Bayesian inference in inverse problems  Youssef Marzouk (Massachusetts Institute of Technology)  Keller Hall 3180  W6.610.11 
3:30pm3:45pm  Coffee break  Keller Hall 3176  W6.610.11  
3:45pm4:15pm  NSF SEES Presentation  Rosemary Renaut (Arizona State University)  Keller Hall 3180  W6.610.11 
4:15pm7:00pm  Social event at Buffalo Wild Wings  Buffalo Wild Wings at Station 19  2001 SE University Avenue Suite 100, Minneapolis, MN 554552195 Phone: 6126179464  W6.610.11 
All Day  Chairs: Karen E. Willcox (Massachusetts Institute of Technology) and Clint N. Dawson (University of Texas at Austin)  W6.610.11  
8:30am9:00am  Coffee  Keller Hall 3176  W6.610.11  
9:00am10:00am  Hierarchical Bayesian Models for Uncertainty Quantification and Model Validation  Roger G. Ghanem (University of Southern California)  Keller Hall 3180  W6.610.11 
10:00am10:30am  Coffee break  Keller Hall 3176  W6.610.11  
10:30am11:30am  Discussion  Keller Hall 3180  W6.610.11  
11:30am1:00pm  Lunch  W6.610.11  
1:00pm2:00pm  An approach for robust segmentation of images from arbitrary Fourier data using l1 minimization techniques  Rosemary Renaut (Arizona State University)  Keller Hall 3180  W6.610.11 
2:00pm2:30pm  Coffee break  Keller Hall 3176  W6.610.11  
2:30pm3:30pm  Bayesian Uncertainty Quantification for Subsurface Inversion using Multiscale Hierarchical Model  Bani K. Mallick (Texas A & M University)  Keller Hall 3180  W6.610.11 
3:30pm4:00pm  Coffee break  Keller Hall 3176  W6.610.11  
4:00pm5:00pm  Climate Variability: Goals and Challenges  Juan Mario Restrepo (University of Arizona)  Keller Hall 3180  W6.610.11 
All Day  Chair: Omar Ghattas (University of Texas at Austin)  W6.610.11  
8:00am8:30am  Coffee  Keller Hall 3176  W6.610.11  
8:30am9:30am  Hierarchical Bayesian Modeling: Why and How  Mark Berliner (Ohio State University)  Keller Hall 3180  W6.610.11 
9:30am9:45am  Coffee break  Keller Hall 3176  W6.610.11  
9:45am10:45am  Surrogate Response Surfaces in Global Optimization and Uncertainty Quantification of Computationally Expensive Simulations with PDE and Environmental Inverse Applications  Christine A. Shoemaker (Cornell University)  Keller Hall 3180  W6.610.11 
10:45am11:00am  Coffee break  Keller Hall 3176  W6.610.11  
11:00am12:00pm  Efficient estimates of prior information and uncertainty with chisquare tests  Jodi L. Mead ()  Keller Hall 3180  W6.610.11 
12:00pm12:05pm  Closing remarks  Keller Hall 3180  W6.610.11 
2:30pm3:00pm  Coffee Break  Lind Hall 400 
2:30pm3:00pm  Coffee Break  Lind Hall 400 
2:30pm3:00pm  Coffee Break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
9:00am10:30am  Lecture 1  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  Lecture 1  Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
12:30pm2:00pm  Lunch  ND6.207.1.11  
2:00pm3:30pm  Lecture 2  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
2:30pm3:00pm  Coffee break  Lind Hall 400  
4:00pm5:30pm  Lecture 1  Àlex Haro Provinciale (University of Barcelona)  Lind Hall 305  ND6.207.1.11 
9:00am10:30am  Lecture 2  Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  Lecture 3  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
12:30pm2:00pm  Lunch  ND6.207.1.11  
2:00pm3:30pm  Lecture 3  Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
2:30pm3:00pm  Coffee break  Lind Hall 400  
4:00pm5:30pm  TBA  George R Sell (University of Minnesota)  Lind Hall 305  ND6.207.1.11 
9:00am10:30am  Lecture 4  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  Lecture 4   Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
12:30pm2:00pm  Lunch  ND6.207.1.11  
2:00pm5:30pm  Afternoon Free  ND6.207.1.11  
2:30pm3:00pm  Coffee break  Lind Hall 400 
9:00am10:30am  Lecture 5  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  Lecture 5  Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
12:30pm2:00pm  Lunch  ND6.207.1.11  
2:00pm3:30pm  Lecture 6  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
2:30pm3:00pm  Coffee break  Lind Hall 400  
4:00pm5:30pm  HuguetLecture 1  Gemma Huguet (Centre de Recerca Matemàtica )  Lind Hall 305  ND6.207.1.11 
9:00am10:30am  Lecture 6  Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  Lecture 7  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
12:30pm2:00pm  Lunch  ND6.207.1.11  
2:00pm3:30pm  Lecture 7  Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
2:30pm3:00pm  Coffee break  Lind Hall 400  
4:00pm5:30pm  Lecture 2  Àlex Haro Provinciale (University of Barcelona)  Lind Hall 305  ND6.207.1.11 
9:00am10:30am  Lecture 8  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  Lecture 8  Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
12:30pm2:00pm  Lunch  ND6.207.1.11  
2:00pm3:30pm  Lecture 2  Gemma Huguet (Centre de Recerca Matemàtica )  Lind Hall 305  ND6.207.1.11 
2:30pm3:00pm  Coffee break  Lind Hall 400  
4:00pm5:30pm  Lecture 3  Àlex Haro Provinciale (University of Barcelona)  Lind Hall 305  ND6.207.1.11 
9:00am10:30am  Lecture 9  Rafael de la Llave (University of Texas at Austin)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  Exchange lemmas  Stephen Schecter (North Carolina State University)  Lind Hall 305  ND6.207.1.11 
12:30pm2:00pm  Lunch  ND6.207.1.11  
2:00pm3:30pm  Lecture 3  Gemma Huguet (Centre de Recerca Matemàtica )  Lind Hall 305  ND6.207.1.11 
2:30pm3:00pm  Coffee break  Lind Hall 400  
4:00pm5:30pm  Lecture 1  Martin WenYu Lo (National Aeronautics and Space Administration (NASA))  Lind Hall 305  ND6.207.1.11 
9:00am10:30am  Lecture 9  Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their Applications  Peter W. Bates (Michigan State University)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  Lecture 2  Martin WenYu Lo (National Aeronautics and Space Administration (NASA))  Lind Hall 305  ND6.207.1.11 
12:30pm5:30pm  Afternoon Free  ND6.207.1.11  
2:30pm3:00pm  Coffee break  Lind Hall 400 
9:00am10:30am  Loss of normal hyperbolicity  Stephen Schecter (North Carolina State University)  Lind Hall 305  ND6.207.1.11 
11:00am12:30pm  TBA  Zeng Lian (New York University)  Lind Hall 305  ND6.207.1.11 
12:30pm2:00pm  Lunch  ND6.207.1.11  
2:00pm3:30pm  Other attendees speak  Lind Hall 305  ND6.207.1.11  
2:30pm3:00pm  Coffee break  Lind Hall 400  
4:00pm5:30pm  Plus open problems  Lind Hall 305  ND6.207.1.11 
9:00am10:30am  Other attendees speak  Lind Hall 305  ND6.207.1.11  
11:00am12:30pm  Plus open problems  Lind Hall 305  ND6.207.1.11  
12:30pm2:00pm  Lunch Break  ND6.207.1.11  
2:30pm3:00pm  Coffee break  Lind Hall 400  
4:00pm5:30pm  depart  Lind Hall 305  ND6.207.1.11 
Moritz Allmaras (Texas A & M University), Yulia Hristova (University of Minnesota)  Poster Detecting small low emission radiating sources 
Abstract: In order to prevent smuggling of highly enriched nuclear material through border controls new advanced detection schemes need to be developed. Typical issues faced in this context are sources with very low emission against a dominating natural background radiation. Sources are expected to be small and shielded and hence cannot be detected from measurements of radiation levels alone. We propose a detection method that relies on the geometric singularity of small sources to distinguish them from the more uniform background. The validity of our approach can be justified using properties of related techniques from medical imaging. Results of numerical simulations are presented for collimated and Comptontype measurements in 2D and 3D.  
Mihai Anitescu (Argonne National Laboratory)  GradientEnhanced Uncertainty Propagation 
Abstract: In this work we discuss an approach for uncertainty propagation
through computationally expensive physics simulation
codes. Our approach incorporates gradient information information
to provide a higher quality surrogate with fewer simulation
results compared with derivativefree approaches. We use this information in two ways: we fit a polynomial or Gaussian process model ("surrogate") of the system response. In a third approach we hybridize the techniques where a Gaussian process with polynomial mean is fit resulting in an improvement of both techniques. The surrogate coupled with input uncertainty information provides a complete uncertainty approach when the physics simulation code can be run at only a small number of times. We discuss various algorithmic choices such as polynomial basis and covariance kernel. We demonstrate our findings on synthetic functions as well as nuclear reactor models. 

Florian Augustin (TU München)  Poster Algorithm Class ARODE 
Abstract: Ordinary differential equations with uncertain parameters are a vast field of research. MonteCarlo simulation techniques are widely used to approximate quantities of interest of the solution of random ordinary differential equations. Nevertheless, over the last decades, methods based on spectral expansions of the solution process have drawn great interest. They are promising methods to efficiently approximate the solution of random ordinary differential equations. Although global approaches on the parameter domain reveal to be very inaccurate in many cases, an elementwise approach can be proven to converge. This poster presents an algorithm, which is based on the stochastic Galerkin RungeKutta method. It incorporates adaptive stepsize control in time and adaptive partitioning of the parameter domain.  
Mark Berliner (Ohio State University)  Hierarchical Bayesian Modeling: Why and How 
Abstract: After a brief review of the hierarchical Bayesian viewpoint, I will present examples of interest in the geosciences. The first is a paleoclimate setting. The problem is to use observed temperatures at various depths and the heat equation to infer surface temperature history. The second combines an elementary physical model with observational data in modeling the flow of the Northeast IceStream in Greenland. The next portion of the talk presents ideas and examples for incorporating output from largescale computer models (e.g., climate system models) into hierarchical Bayesian models.  
Andrew J. Booker (Boeing)  Uncertainty Quantification and Optimization Under Uncertainty: Experience and Challenges 
Abstract: This talk will describe experiences and challenges at Boeing with Uncertainty Quantification (UQ) and Optimization Under Uncertainty (OUU) in conceptual design problems that use complex computer simulations. The talk will describe tools and methods that have been developed and used by the Applied Math group at Boeing and their perceived strengths and limitations. Application of the tools and methods will be illustrated with an example in conceptual design of a hypersonic vehicle. Finally I will discuss future development plans and needs in UQ and OUU.  
Tan BuiThanh (University of Texas at Austin)  Poster  Scalable parallel algorithms for uncertainty quantification in high dimensional inverse problems 
Abstract: Quantifying uncertainties in largescale forward and inverse PDE
simulations has emerged as the central challenge facing the field of
computational science and engineering. In particular, when the forward
simulations require supercomputers, and the uncertain parameter
dimension is large, conventional uncertainty quantification methods
fail dramatically. Here we address uncertainty quantification in
largescale inverse problems. We adopt the Bayesian inference
framework: given observational data and their uncertainty, the
governing forward problem and its uncertainty, and a prior probability
distribution describing uncertainty in the parameters, find the
posterior probability distribution over the parameters. The posterior
probability density function (pdf) is a surface in high dimensions,
and the standard approach is to sample it via a Markovchain Monte
Carlo (MCMC) method and then compute statistics of the
samples. However, the use of conventional MCMC methods becomes
intractable for high dimensional parameter spaces and
expensivetosolve forward PDEs. Under the Gaussian hypothesis, the mean and covariance of the posterior distribution can be estimated from an appropriately weighted regularized nonlinear least squares optimization problem. The solution of this optimization problem approximates the mean, and the inverse of the Hessian of the least squares function (at this point) approximates the covariance matrix. Unfortunately, straightforward computation of the nominally dense Hessian is prohibitive, requiring as many forward PDElike solves as there are uncertain parameters. However, the data are typically informative about a low dimensional subspace of the parameter space. We exploit this fact to construct a low rank approximation of the Hessian and its inverse using matrixfree Lanczos iterations, which typically requires a dimensionindependent number of forward PDE solves. The UQ problem thus reduces to solving a fixed number of forward and adjoint PDE problems that resemble the original forward problem. The entire process is thus scalable with respect to forward problem dimension, uncertain parameter dimension, observational data dimension, and number of processor cores. We apply this method to the Bayesian solution of an inverse problem in 3D global seismic wave propagation with tens of thousands of parameters, for which we observe two orders of magnitude speedups. 

Julianne Chung (University of Maryland)  Poster Designing Optimal Spectral Filters for Inverse Problems 
Abstract: Spectral filtering suppresses the amplification of errors when computing solutions to illposed inverse problems; however, selecting good regularization parameters is often expensive. In many applications, data is available from calibration experiments. In this poster, we describe how to use this data to precompute optimal spectral filters. We formulate the problem in an empirical Bayesian risk minimization framework and use efficient methods from stochastic and numerical optimization to compute optimal filters. Our formulation of the optimal filter problem is general enough to use a variety of error metrics, not just the mean square error. Numerical examples from image deconvolution illustrate that our proposed filters perform consistently better than wellestablished filtering methods.  
Louis J. Durlofsky (Stanford University)  Data Assimilation and Efficient Forward Modeling for Subsurface Flow 
Abstract: In this talk I will present computational procedures applicable for the realtime modelbased management and optimization of subsurface flow operations such as oil production and geological carbon storage. Specifically, the use of kernel principal component analysis (KPCA) for representing geostatistical models in data assimilation procedures and the use of reducedorder models for efficient flow simulations will be described. KPCAbased representations will be shown to better capture multipoint spatial statistics, which gives them an advantage over standard KarhunenLoeve procedures for representing complex geological systems. The use of KPCA within a gradientbased data assimilation (history matching) procedure will be illustrated. Next, a reducedorder modeling technique applicable for forward simulations will be described. This approach, called trajectory piecewise linearization (TPWL), entails linearization around previously simulated states and projection into a lowdimensional subspace using proper orthogonal decomposition. The method requires training runs that are performed using a fullorder model, though subsequent simulations are very fast. The performance of the TPWL approach and its use in optimization will be demonstrated for realistic field problems.  
Richard Dwight (Delft University of Technology)  Poster Bayesian Inference for Data Assimilation using LeastSquares Finite Element Methods 
Abstract: It has recently been observed that LeastSquares Finite Element methods (LSFEMs) can be used to assimilate experimental data into approximations of PDEs in a natural way. The approach was shown to be effective without regularization terms, and can handle substantial noise in the experimental data without filtering. Of great practical importance is that  it is not significantly more expensive than a single physical simulation. However the method as presented so far in the literature is not set in the context of an inverse problem framework, so that for example the meaning of the final result is unclear. In this paper it is shown that the method can be interpreted as finding a maximum a posteriori (MAP) estimator in a Bayesian approach to data assimilation, with normally distributed observational noise, and a Bayesian prior based on an appropriate norm of the governing equations. In this setting the method may be seen to have several desirable properties: most importantly discretization and modelling error in the simulation code does not affect the solution in limit of complete experimental information, so these errors do not have to be modelled statistically. Also the Bayesian interpretation better justifies the choice of the method, and some useful generalizations become apparent. The technique is applied to incompressible NavierStokes flow in a pipe with added velocity data, where its effectiveness, robustness to noise, and application to inverse problems is demonstrated.  
Virginie Ehrlacher (École des Ponts ParisTech)  Poster  Convergence of a greedy algorithm for highdimensional convex nonlinear problems 
Abstract: In this work, we present a greedy algorithm based on a tensor product decomposition, whose aim is to compute the global minimum of a strongly convex energy functional. We prove the convergence of our method provided that the gradient of the energy is Lipschitz on bounded sets. This is a generalization of the result which was proved by Le Bris, Lelievre and Maday (2009) in the case of a linear high dimensional Poisson problem. The main interest of this method is that it can be used for high dimensional nonlinear convex problems. We illustrate this algorithm on a prototypical example for uncertainty propagation on the obstacle problem.  
Colin Fox (University of Otago)  The best we can do with MCMC, and how to do better. 
Abstract: Samplebased inference is a great way to summarize inverse and predictive distributions arising in largescale applications. The best current technology for drawing samples are the MCMC algorithms, with the latest algorithms enabling comprehensive solution of substantial geophysical problems. However, for the largestscale applications the geometric convergence of MCMC needs to be improved upon. A source of ideas are the algorithms from computational optimization. Developing the computational science of sampling algorithms is essential, for which a suite of test problems, using lowlevel midlevel and highlevel representations, could be useful in focusing efforts in the community.  
Roger G. Ghanem (University of Southern California)  The Curse of Dimensionality, Model Validation, and UQ. 
Abstract: The curse of dimensionality is a ubiquitous challenge in uncertainty quantification. It usually comes about as the complexity of analysis is controlled by the complexity of input parameters. In most cases of practical relevance, the output quantity of interest (QoI) is some integral of the input quantities and can thus be described in a much lower dimensional setting. This talk will describe novel procedures for honoring the lowdimensional character of the QoI without any loss of information. The talk will also describe the range of QoI that can be addressed using this formalism. The role of UQ as the engine behind the model validation puts a burden of rigor on UQ formulations. The ability to explore the effect of particular probabilistic choices on model validity is paramount for practical applications in general, and datapoor applications in particular. The talk will also address achievable and meaningful definitions of the validation process and demonstrate their relevance in the context of industrial problems. 

Roger G. Ghanem (University of Southern California)  Hierarchical Bayesian Models for Uncertainty Quantification and Model Validation 
Abstract: Recent developments with polynomial chaos expansions with random coefficients facilitate the accounting for subscale features, not captured in standard probabilistic models. These representations provide a geometric characterization of random variables and processes, which is quite distinct from the characterizations (in terms of probability density functions) typically adapted to Bayesian analysis. Given the importance of Bayes theorem within probability theory, it is important to synthesize the connection between these two representations. In this talk, we will describe a hierarchical Bayesian framework that introduces polynomial chaos expansions with random parameters as a consequence of Bayesian data assimilation. We will provide insight into the behavior and use of these expansions and exemplify them through a multiscale application from thermal science. Specifically, information collected from fine scale simulations is used to construct stochastic reduced order models. These coarse models are indexed in terms of specimentospecimen variability and also in terms of variability in their subscale features. The ability of these doublystochastic expansions to improve the predictive value of modelbased simulations is highlighted.  
Omar Ghattas (University of Texas at Austin), Karen E. Willcox (Massachusetts Institute of Technology)  Workshop Introduction 
Abstract: This lecture provides an introduction to the IMA Workshop on Largescale Inverse Problems and Quantification of Uncertainty. We present context and motivation for the workshop topic along with a discussion of open research challenges. We will discuss workshop goals and provide a brief overview of the workshop schedule.  
Albert B. Gilg (Siemens)  Mastering Impact of Uncertainties by Robust Design Optimization Techniques for TurboMachinery 
Abstract: Deterministic design optimization approaches are no longer satisfactory for industrial high technology products. Product and process designs often exploit physical limits to improve performance. In this regime uncertainty originating from fluctuations during fabrication and small disturbances in system operations severely impacts product performance and quality. Design robustness becomes a key issue in optimizing industrial designs. We present challenges and solution approaches implemented in our robust design tool RoDeO applied turbo charger design. In addition to the challenges for electricity generating turbines, turbo chargers have to work efficiently for a wide range of rotation frequencies. Timeconsuming aerodynamic (CFD) and mechanical (FEM) computations for large sets of frequencies became a severely limiting factor even for deterministic optimization. Further more constrained deterministic optimization could not guarantee critical design limits under impact of uncertainty during fabrication. Especially, the treatment of design constraints in terms of thresholds for von Mises stress or modal frequencies became crucial. We introduce an efficient approach for the numerical treatment of such chance constraints that even do not need additional CFD and FEM calculations in our robust design tool set. An outlook for further design challenges concludes the presentation. Contents of this presentation are joint work of U. Wever, M. Klaus, M. Paffrath and A. Gilg.  
Albert B. Gilg (Siemens), Utz Wever (Siemens)  Poster Robust Design for Industrial Applications 
Abstract: Industrial product and process designs often exploit physical limits to improve performance. In this regime uncertainty originating from fluctuations during fabrication and small disturbances in system operations severely impacts product performance and quality. Design robustness becomes a key issue in optimizing industrial designs. We present examples of challenges and solution approaches implemented in our robust design tool RoDeO.  
Albert B. Gilg (Siemens), Utz Wever (Siemens)  Poster Robust Design for Industrial Applications 
Abstract: Industrial product and process designs often exploit physical limits to improve performance. In this regime uncertainty originating from fluctuations during fabrication and small disturbances in system operations severely impacts product performance and quality. Design robustness becomes a key issue in optimizing industrial designs. We present examples of challenges and solution approaches implemented in our robust design tool RoDeO.  
Eldad Haber (University of British Columbia)  Design of simultaneous source 
Abstract: In recent years a new data collection approach has been proposed for geophysical exploration. Rather than recording data for each source separately, sources are shot simultaneously and the combined data is recorded. The question we answer in this talk is, what should be the pattern of shots in order to optimally recover the earth's parameters. To answer the question we use experimental design methodology and show how to efficiently solve the resulting optimization problem  
David Higdon (Los Alamos National Laboratory)  Bayesian approaches for combining computational model output and physical observations 
Abstract: A Bayesian formulation adapted from Kennedy and O'Hagan (2001) and Higdon et al. (2008) is used to give parameter constraints from physical observations and a limited number of simulations. The framework is based on the idea of replacing the simulator by an emulator which can then be used to facilitate computations required for the analysis. In this talk I'll describe the details of this approach and apply it to an example that uses large scale structure of the universe to inform about a subset of the parameters controlling a cosmological model. I'll also explain basics of using Gaussian process models and compare them to an approach that uses the ensemble Kalman filter.  
Charles S. Jackson (University of Texas at Austin)  Scientific and statistical challenges to quantifying uncertainties in climate projections 
Abstract: The problem of estimating uncertainties in climate prediction is not well defined. While one can express its solution within a Bayesian statistical framework, the solution is not necessarily correct. One must confront the scientific issues for how observational data is used to test various hypotheses for the physics of climate. Moreover, one also must confront the computational challenges of estimating the posterior distribution without the help of a statistical emulator of the forward model. I will present results of a recently completed estimate of the uncertainty in specifying 15 parameters important to clouds, convection, and radiation of the Community Atmosphere Model. I learned that the maximum posterior probably is not in the same region of parameter space as the minimum loglikelihood. I have interpreted these differences to the existence of model biases and the potential that the minimum loglikelihood, which are often the desired solutions to data inversion problems, are overfitting the data. Such a result highlights the need for a combination of scientific and computational thinking to begin to address uncertainties for complex multiphysics phenomena.  
Charles S. Jackson (University of Texas at Austin)  Poster  Scientific and statistical challenges to quantifying uncertainties in climate projections 
Abstract: The problem of estimating uncertainties in climate prediction is not well defined. While one can express its solution within a Bayesian statistical framework, the solution is not necessarily correct. One must confront the scientific issues for how observational data is used to test various hypotheses for the physics of climate. Moreover, one also must confront the computational challenges of estimating the posterior distribution without the help of a statistical emulator of the forward model. I will present results of a recently completed estimate of the uncertainty in specifying 15 parameters important to clouds, convection, and radiation of the Community Atmosphere Model. I learned that the maximum posterior probably is not in the same region of parameter space as the minimum loglikelihood. I have interpreted these differences to the existence of model biases and the potential that the minimum loglikelihood, which are often the desired solutions to data inversion problems, are overfitting the data. Such a result highlights the need for a combination of scientific and computational thinking to begin to address uncertainties for complex multiphysics phenomena.  
Jan Dirk Jansen (Delft University of Technology)  Systemtheoretical aspects of oil and gas reservoir history matching 
Abstract: 'History matching' of reservoir models by adapting model parameters such that the model ouput matches historic production data is known to be a very illposed problem. I will discuss the limited observability and controllability of reservoir states (pressures, fluid saturations) and limited identifiability of reservoir parameters (permeabilities, porosities, etc.). I'll present results from our group in Delft including a method to use the remaining freedom in the parameter space after history matching to obtain upper and lower bounds for the prediction of oil recovery from the updated reservoir model.  
Bangti Jin (Texas A & M University)  Poster  Sparsity reconstruction in electrical impedance tomography 
Abstract: Electrical impedance tomography is a diffusive imaging modality for determining the conductivity distributions of an object from boundary measurements. We here propose a novel reconstruction algorithm based on Tikhonov regularization with sparsity constraints. The wellposedness of the formulation, and convergence rates results are established. Numerical experiments for simulation and real data are presented to illustrate the effectiveness of the approach.  
Gardar Johannesson (Lawrence Livermore National Laboratory)  Poster The Uncertainty Quantification Project at Lawrence Livermore National Laboratory: Sensitivities and Uncertainties of the Community Atmosphere Model 
Abstract: A team at the Lawrence Livermore National Laboratory is currently undertaking an uncertainty analysis of the Cummunity Earth System Model (CESM), as a part of a larger effort to advance the science of Uncertainty Quantification (UQ). The Climate UQ effort has three major phases: UQ of the Cummunity Atmospheric Model (CAM) component of CESM, UQ of CAM coupled to a simple slab ocean model, and UQ of the fully coupled CESM (CAM + 3D ccean). In this poster we describe the first phase of the Climate UQ effort; the generate of CAM ensemble of simulations for sensitivity and uncertainty analysis.  
Donald R. Jones (General Motors)  Improved Quantification of Prediction Error for Kriging Response Surfaces 
Abstract: Kriging response surfaces are now widely used to optimize design parameters in industrial applications where assessing a design's performance requires long computer simulations. The typical approach starts by running the computer simulations at points in an experiment design and then fitting kriging surfaces to the resulting data. One then proceeds iteratively: calculations are made on the surfaces to select new point(s); the simulations are run at these points; and the surfaces are updated to reflect the results. The most advanced approaches for selecting new points for sampling balance sampling where the kriging predictor is good (local search) with sampling where the kriging mean squared error is high (global search). Putting some emphasis on searching where the error is high ensures that we improve the accuracy of the surfaces between iterations and also makes the search global. A potential problem with these approaches, however, is that the classic formula for the kriging mean squared error underestimates the true error, especially in small samples. The reason is that the formula is derived under the assumption that the parameters of the underlying stochastic process are known, but in reality they are estimated. In this paper, we show how to fix this underestimation problem and explore how doing so affects the performance of krigingbased optimization methods. 

Hector Klie (ConocoPhillips)  Poster A Multiscale Learning Approach for History Matching 
Abstract: The present work describes a machine learning approach for performing history matching. It consists of a hybrid multiscale search methodology based on SVD and the wavelet transform to incrementally reduce the parameter space dimensionality. The parameter space is globally explored and sampled by the simultaneous perturbation stochastic approximation (SPSA) algorithm at a different resolution scales. At a sufficient degree of coarsening, the parameters are estimated with the aid of an artificial neural network. The neural network serves also as a convenient device to evaluate the sensitiveness of the objective function with respect to variations of each individual model parameter in the vicinity of a promising optimal solution. Preliminary results shed light on future research avenues for optimizing the use of additional sources of information such as seismic or timely sensor data in history matching procedures. This work has been developed in collaboration with Adolfo Rodriguez (Subsurface Technology, ConocoPhillips) and Mary F. Wheeler (Center for Subsurface Modeling, University of Texas at Austin) 

Pierre FJ Lermusiaux (Massachusetts Institute of Technology)  Ocean Uncertainty Prediction and nonGaussian Data Assimilation with Stochastic PDEs: ByeBye MonteCarlo? 
Abstract: Uncertainty predictions and data assimilation for ocean and fluid flows are discussed within the context of Dynamically Orthogonal (DO) field equations and their adaptive error subspaces. These stochastic partial differential equations provide prior probabilities for novel nonlinear data assimilation methods which are derived and illustrated. The use of these nonlinear data assimilation methods and DO equations for targeted observations, i.e. for predicting the optimal sampling plans, is discussed. Numerical aspects are summarized, including new consistent schemes and test cases for the discretization of DO equations. Examples are provided using timedependent ocean and fluid flows in two spatial dimensions. Coauthors from our MSEAS group at MIT: Thomas Sondergaard, Themis Sapsis, Matt Ueckermann and Tapovan Lolla 

Guang Lin (Pacific Northwest National Laboratory)  Poster  Error Reduction and Optimal Parameters Estimation in Convective Cloud Scheme in Climate Model 
Abstract: In this work, we studied sensitivity of physic processes and simulations to parameters in climate model, reduced errors and derived optimal parameters used in cloud convection scheme. MVFSA method is employed to derive optimal parameters and quantify the climate uncertainty. Through this study, we observe that parameters such as downdraft, entrainment and cape consumption time have very important impact on convective precipitation. Although only precipitation is constrained in this study, other climate variables are controlled by the selected parameters so could be beneficial by the optimal parameters used in convective cloud scheme.  
Quan Long (King Abdullah University of Science & Technology)  Poster Information Gain in Model Validation for Porous Media 
Abstract: In this work, we use the relative entropy of the posterior probability density function (PPDF) to measure the information gain in the Bayesian model validation procedure. The entropies related to different groups of validation data are compared and we subsequently choose the validation data with the most information gain (Principle of Maximum Entropy) to predict a quantity of interest in the more complicated prediction case. The proposed procedure is independent of any model related assumption, therefore enabling an objective decision making on the rejection/adoption of cali brated models. This work can be regarded as an extension to the Bayesian model validation method proposed by [Babusˇka et al.(2008)]. We illustrate the methodology on an numerical example dealing with the validation of models for porous media. Specifically the effective permeability of a 2D porous media is calibrated and validated. We use here synthetic data obtained by computer simulations of the Navier Stokes equation  
Bani K. Mallick (Texas A & M University)  Bayesian Uncertainty Quantification for Subsurface Inversion using Multiscale Hierarchical Model 
Abstract: We present a Bayesian approach to to nonlinear inverse problems in which the unknown quantity is a random field (spatial or temporal). The Bayesian approach contains a natural mechanism for regularization in the form of prior information, can incorporate information from from heterogeneous sources and provide a quantitative assessment of uncertainty in the inverse solution. The Bayesian setting casts the inverse solution as a posterior probability distribution over the model parameters. KarhunenLo'eve expansion is used for dimension reduction of the random field. Furthemore, we use a hierarchical Bayes model to inject multiscale data in the modeling framework. In this Bayesian framework, we have shown that this inverse problem is wellposed by proving that the posterior measure is Lipschitz continuous with respect to the data in total variation norm. Computation challenges in this construction arise from the need for repeated evaluations of the forward model (e.g. in the context of MCMC) and are compounded by high dimensionality of the posterior. We develop twostage reversible jump MCMC which has the ability to screen the bad proposals in the first inexpensive stage. Numerical results are presented by analyzing simulated as well as real data.  
María Gabriela Martínez López (Stevens Institute of Technology)  Poster Stochastic TwoStage Problems with Stochastic Dominance Constraint 
Abstract: We analyze stochastic twostage optimization problems with a stochastic dominance constraint on the recourse function. The dominance constraint provides risk control on the future cost. The dominance relation is represented by either the Lorenz functions or by the expected excess functions of the random variables. We propose two decomposition methods to solve the problem and prove their convergence. Our methods exploit the decomposition structure of the expected value twostage problems and construct successive approximations of the stochastic dominance constraint.  
Youssef Marzouk (Massachusetts Institute of Technology)  A mapbased approach to Bayesian inference in inverse problems 
Abstract: Bayesian inference provides a natural framework for quantifying uncertainty in PDEconstrained inverse problems, for fusing heterogeneous sources of information, and for conditioning successive predictions on data. In this setting, simulating from the posterior via Markov chain Monte Carlo (MCMC) constitutes a fundamental computational bottleneck. We present a new technique that entirely avoids Markov chainbased simulation, by constructing a map under which the posterior becomes the pushforward measure of the prior. Existence and uniqueness of a suitable map is established by casting our algorithm in the context of optimal transport theory. The proposed maps are analytically and efficiently computed using various optimization methods.  
Jodi L. Mead  Efficient estimates of prior information and uncertainty with chisquare tests 
Abstract: Many practical inverse problems are illposed, involve large amounts of data and have high dimensional parameter spaces. It is necessary to include uncertainty both to regularize the problem and account for errors in the data and model. However, when processes are modeled as random, a complete treatment of uncertainty requires specification of prior probability distributions for data or parameters. In this work statistical information in the form of uncertainty in parameters and state variables is assumed and propagated, however, the underlying probability distributions do not need to be specified or calculated. This results in an efficient approach to largescale, illposed inverse problems. Even though prior probability distributions are not necessarily specified, we are required to specify prior knowledge in the form of second moments or variances. We estimate these by applying chisquare tests to calculate the second moment of the error in a model, an initial parameter estimate, or data. Efficient newtontype algorithms have been developed to calculate regularization parameters, and estimate the standard deviation of data error. More recently, we have used chisquare tests to calculate diagonal error covariance matrices and these can be used to obtain nonsmooth least squares solutions. Finally, we have developed the chisquared method for nonlinear problems and will show some recent results. Applications with the chisquare method includes soil moisture estimation, lagrangian flow, and threat detection. 

Dimitrios Mitsotakis (University of Minnesota)  PosterA hybrid numerical method for the numerical solution of the Benjamin equation 
Abstract: Because Benjamin equation has a spatial structure somewhat like that of the Korteweg–de Vries equation, explicit schemes have unacceptable stability limitations. We instead implement a highly accurate, unconditionally stable scheme that features a hybrid Galerkin FEM/pseudospectral method with periodic splines to approximate the spatial structure and a twostage Gauss–Legendre implicit RungeKutta method for the temporal discretization. We present several numerical experiments shedding light in some properties of the solitary wave solutions for the specific equation.  
Dianne P. O'Leary (University of Maryland)  Confidence in Image Reconstruction 
Abstract: Forming the image from a CAT scan and taking the blur out of vacation pictures are problems that are illposed. By definition, small changes in the data to an illposed problem make arbitrarily large changes in the solution. How can we hope to solve such problems when data are noisy and computer arithmetic is inexact? In this talk we discuss the use of calibration data, side conditions, and bias constraints to improve the quality of solutions and our confidence in the results. Some of this work is joint with Julianne Chung, Matthias Chung, James Nagy, and Bert Rust. 

Dean S. Oliver (University of Bergen)  Ensemblebased methods: filters, smoothers and iteration 
Abstract: For many largescale nonlinear inverse problems, Monte Carlo methods provide the only practical method of quantifying uncertainty. Ensemblebased methods such as the ensemble Kalman filter and ensemble smoothers have found increasing application in data assimilation systems for weather prediction, oceanography, and subsurface flow. In this talk, I will describe the methods in general, their connection with GaussNewton minimization methods and the approach to sampling. The methodology will be illustrated with several fairly largescale examples from subsurface flow.  
Henning Omre (Norwegian University of Science and Technology (NTNU))  Spatial categorical inversion: Seismic inversion into lithology/fluid classes 
Abstract: Modeling of discrete variables in a threedimensional reference space is a challenging problem. Constraints on the model expressed as invalid local combinations and as indirect measurements of spatial averages add even more
complexity. Evaluation of offshore petroleum reservoirs covering many square kilometers and buried at several kilometers depth contain problems of this type. Focus is on identification of hydrocarbon (gas or oil) pockets in the subsurface  these appear as rare events. The reservoir is classified into lithology (rock)classes  shale and sandstone  and the latter contains fluids  either gas, oil or brine (salt water). It is known that these classes are vertically thin with large horizontal continuity. The reservoir is considered to be in equilibrium  hence fixed vertical sequences of fluids  gas/oil/brine  occur due to gravitational sorting. Seismic surveys covering the reservoir is made and through processing of the data, angledependent amplitudes of reflections are available. Moreover, a few wells are drilled through the reservoir and exact observations of the reservoir properties are collected along the well trace. The inversion is phrased in a hierarchical Bayesian inversion framework. The prior model, capturing the geometry and ordering of the classes, is of Markov random field type. A particular parametrization coined Profile Markov random field is defined. The likelihood model linking lithology/fluids and seismic data captures major characteristics of rock physics models and the wave equation. Several parameters in this likelihood model is considered to be stochastic and they are inferred from seismic data and observations along the well trace. The posterior model is explored by an extremely efficient McMCalgorithm. The methodology is defined and demonstrated on observations from a real North Sea reservoir. Coauthor: Kjartan Rimstad, Department of Mathematical Sciences, NTNU, Trondheim, Norway 

George C. Papanicolaou (Stanford University)  Uncertainty quantification of shock interactions with complex environments 
Abstract: Many issues in uncertainty quantification, as they emerge from the perspective of large scale scientific computations of increasing complexity, involve dealing with stochastic versions of the basic equations modeling the phenomena of interest. A common reaction is to generate samples of solutions by choosing parameters randomly and computing solutions repeatedly. It is quickly realized that this is much too computationally demanding (but not entirely useless). Another common reaction is to do a sensitivity analysis by varying parameters in the neighborhood of regions of interest, leading to adjoint methods and computations that are not much more demanding than the basic one for which we want to find error bars. One does not have to be a sophisticated probabilist or statistician to realize that there is room for some interdisciplinary research here. My experience in studying waves and diffusion in random media motivated me to look into uncertainty quantification and to address some of the emerging issues. One such issue is the study of the propagation of shock profiles in random (turbulent) media. I will introduce this problem and analyze it from the point of view of large deviations, which is a regime that is particularly difficult to explore numerically. This problem is of independent interest in stochastic analysis and provides an example of how ideas from this theoretical research area can be used in applications. This is joint work with J. Garnier and T.W. Yang.  
Roland Pulch (Bergische UniversitätGesamthochschule Wuppertal (BUGH))  Poster  Polynomial Chaos for Differential Algebraic Equations with Random Parameters 
Abstract: Mathematical modeling of industrial applications often yields timedependent systems of differential algebraic equations (DAEs) like in the simulation of electric circuits or in multibody dynamics for robotics and vehicles. The properties of a system of DAEs are characterized by its index. The DAEs include physical parameters, which may exhibit uncertainties due to measurements, for example. For a quantification of the uncertainties, we replace the parameters by random variables. The resulting stochastic model can be resolved by methods based on the polynomial chaos, where either a stochastic collocation or the stochastic Galerkin technique is applied. We analyze the index of the larger coupled system of DAEs, which has to be solved in the stochastic Galerkin method. Moreover, we present results of numerical simulations, where a system of DAEs corresponding to an electric circuit is used as test example.  
Grant Reinman (Pratt & Whitney)  Design For Variation at Pratt & Whitney 
Abstract: Pratt & Whitney is a large aerospace company involved in the design and manufacture of commercial and military aircraft engines, rocket engines and space propulsion systems. This talk describes Pratt & Whitney's vision, strategy, and current state of their large scale implementation of probabilistic methods in engineering. Key technologies and methods are described, as well as the challenges that lie ahead of us. We will emphasize that (1) Probabilistic analysis and design are complex interdisciplinary undertakings, and (2) Methods and computational tools have been developed since 2001 that enable us to more efficiently perform model emulation, sensitivity and uncertainty analyses.  
Rosemary Renaut (Arizona State University)  An approach for robust segmentation of images from arbitrary Fourier data using l1 minimization techniques 
Abstract: I will review approaches for detecting edges from Fourier data. Application to cases where the data is noisy, blurred, or partially missing, requires use of a regularization term, and accompanying regularization parameter. Our analysis focuses on validation through robustness with respect to correctly classifying edge data. Note that in this method, segmentation is achieved without reconstruction of the underlying image.  
Rosemary Renaut (Arizona State University)  NSF SEES Presentation 
Abstract: The NSF has a new focus on issues relating to Sustainability sciences. I will provide a short overview of existing solicitations and plans for the future. The main intent of this short presentation is to increase awareness in our community of these upcoming opportunities. Mainly I will direct you to numerous publicly available links concerning these plans for funding Science, Engineering and Education activities for attaining a Sustainable Future.  
Juan Mario Restrepo (University of Arizona)  Climate Variability: Goals and Challenges 
Abstract: A fundamental challenge in climate science is to make sense of very limited and poorly constrained data. Even though many data gathering campaigns are taking pl ace or are being planned, the very high dimensional state space of the system ma kes the prospects of climate variability analysis from data alone very tenuous, especially in the near term. The use of models and data, via data assimilation, is one of the strategies pursued to improve climate predictions and retrodiction s. I will review some of the challenges with this process, cover some of our gro up's efforts to meet these. I wil also enumerate a prioritized list of problems, which if addressed with careful mathematical treatment, will have a significant impact on climate variability understanding.  
Werner Römisch (HumboldtUniversität)  Scenario generation in stochastic programming with application to optimizing electricity portfolios under uncertainty 
Abstract: We review some recent advances in highdimensional numerical integration, namely, in (i) optimal quantization of probability distributions, (ii) QuasiMonte Carlo (QMC) methods, (iii) sparse grid methods. In particular, the methods (ii) and (iii) may be superior compared to Monte Carlo (MC) methods under certain conditions on the integrands. Some related open questions are also discussed. In the second part of the talk we present a model for optimizing electricity portfolios under demand and price uncertainty and argue that electricity companies are interested in riskaverse decisions. We explain how the stochastic data processes are modeled and how scenarios may be generated by QMC methods followed by a tree generation procedure. We present solutions for the riskneutral and riskaverse situation, discuss the costs of risk aversion and provide several possibilities for risk aversion by multiperiod risk measures.  
Christine A. Shoemaker (Cornell University)  Surrogate Response Surfaces in Global Optimization and Uncertainty Quantification of Computationally Expensive Simulations with PDE and Environmental Inverse Applications 
Abstract: Solving inverse problems for nonlinear simulation models with nonlinear objective is usually a global optimization problem. This talk will present an overview of the development of algorithms that employ response surfaces as a surrogate for an expensive simulation model to significantly reduce the computational effort required to solve continuous global optimization problems and uncertainty analysis of simulation models that require a substantial amount of CPU time for each simulation. I will show that for many cases of nonlinear simulation models, the resulting optimization problem is multimodal and hence requires a global optimization method. In order to reduce the number of simulations required, we are interested in utilizing information from all previous simulations done as part of an optimization search by building a (radial basis function) multivariate response surface that interpolates these earlier simulations. I will discuss the alternative approaches of direct global optimization search versus using a multistart method in combination with a local optimization method. I will also describe an uncertainty analysis method SOARS that uses derivativefree optimization to help construct a response surface of the likelihood function to which Markov Chain Monte Carlo is applied. This approach has been shown to reduce CPU requirements to less than 1/65 of what is required by conventional MCMC uncertainty analysis. I will present examples of the application of these methods to significant environmental problems described by computationally intensive simulation models used worldwide. One model (TOUGH2) involves partial differential equation models for fluid flow for carbon sequestration and the second is SWAT, which is used to describe potential pollution of NYC’s drinking water. In both cases, the model uses sitespecific data. This work has been a collaboration with others including: R. Regis and Y. Wang (Optimization), N. Bliznyuk and D. Ruppert (uncertainty), A. Espinet and J. Woodbury (Environmental Applications) 

Laura Swiler (Sandia National Laboratories)  Multiple Model Inference: Calibration and Selection with Multiple Models 
Abstract: This talk compares three approaches for model selection: classical least squares methods, information theoretic criteria, and Bayesian approaches. Least squares methods are not model selection methods although one can select the model that yields the smallest sumofsquared error function. Information theoretic approaches balance overfitting with model accuracy by incorporating terms that penalize more parameters with a loglikelihood term to reflect goodness of fit. Bayesian model selection involves calculating the posterior probability that each model is correct, given experimental data and prior probabilities that each model is correct. As part of this calculation, one often calibrates the parameters of each model and this is included in the Bayesian calculations. Our approach is demonstrated on a structural dynamics example with models for energy dissipation and peak force across a bolted joint. The three approaches are compared and the influence of the loglikelihood term in all approaches is discussed.  
Nicolae Tarfulea (Purdue University, Calumet)  Poster Modeling and Analysis of HIV Evolution and Therapy 
Abstract: We present a mathematical model to investigate theoretically and numerically the effect of immune effectors, such as the cytotoxic lymphocyte (CTL), in modeling HIV pathogenesis during primary infection. Additionally, by introducing drug therapy, we assess the effect of treatments consisting of a combination of several antiretroviral drugs. Nevertheless, even in the presence of drug therapy, ongoing viral replication can lead to the emergence of drugresistant virus variances. Thus, by including two viral strains, wildtype and drugresistant, we show that the inclusion of the CTL compartment produces a higher rebound for an individual’s healthy helper Tcell compartment than does drug therapy alone. We characterize successful drugs or drug combination scenarios for both strains of virus.  
Gabriel Alin Terejanu (University of Texas at Austin)  Poster An Information Theoretic Approach to Model Calibration and Validation using QUESO 
Abstract: The need for accurate predictions arise in a variety of critical
applications such as climate, aerospace and defense. In this work two
important aspects are considered when dealing with predictive
simulations under uncertainty: model selection and optimal
experimental design. Both are presented from an information theoretic
point of view. Their implementation is supported by the QUESO library,
which is a collection of statistical algorithms and programming
constructs supporting research into the uncertainty quantification
(UQ) of models and their predictions. Its versatility has permitted
the development of applications frameworks to support model selection
and optimal experimental design for complex models. A predictive Bayesian model selection approach is presented to discriminate coupled models used to predict an unobserved quantity of interest (QoI). It is shown that the best coupled model for prediction is the one that provides the most robust predictive distribution for the QoI. The problem of optimal data collection to efficiently learn the model parameters is also presented in the context of Bayesian analysis. The preferred design is shown to be where the statistical dependence between the model parameters and observables is the highest possible. Here, the statistical dependence is quantified by mutual information and estimated using a knearest neighbor based approximation. Two specific applications are briefly presented in the two contexts. The selection of models when dealing with predictions of forced oscillators and the optimal experimental design for a graphite nitridation experiment. 

Liping Wang (General Electric)  Challenges In Uncertainty, Calibration, Validation and Predictability of Engineering Analysis Models 
Abstract: Model calibration, validation, prediction and uncertainty quantification have progressed remarkably in the past decade. However, many issues remain. This talk attempts to provide answers to the key questions: 1) how far have we gone? 2) what technical challenges remain? and 3) what are the future directions? Based on a comprehensive literature review from academic, industrial and government research and experience gained at the General Electric (GE) Company, we will summarize the advancements of methods and the applications of these methods to calibration, validation, prediction and uncertainty quantification. The latest research and application thrusts in the field will emphasize the extension of the Bayesian framework to validation of engineering analysis models. Closing remarks will offer insight into possible technical solutions to the challenges and future research directions.  
Dongbin Xiu (Purdue University)  Efficient UQ algorithms for practical systems 
Abstract: Uncertainty quantification has been an active fields in recent years, and many numerical algorithms have been developed. Many research efforts have focused on how to improve the accuracy and error control of the UQ algorithms. To this end, methods based on polynomial chaos have established themselves as the more feasible approach. Despite the fast development from the computational sciences perspective, significant challenges still exist for UQ to be useful in practical systems. One prominent difficulty is the simulation cost. In many practical systems one can afford only a very limited number of simulations. And this prevents one from using many of the existing UQ algorithms. In this talk we discuss the importance of such a challenge and some of the early efforts to address it. 
Moritz Allmaras  Texas A & M University  6/4/2011  6/12/2011 
Sergio Almada Monter  Georgia Institute of Technology  6/1/2011  6/10/2011 
Mark C Anderson  Los Alamos National Laboratory  6/5/2011  6/10/2011 
Mihai Anitescu  Argonne National Laboratory  6/1/2011  6/4/2011 
Douglas N. Arnold  University of Minnesota  9/1/2010  6/30/2011 
Florian Augustin  TU München  6/1/2011  6/12/2011 
Gerard Michel Awanou  Northern Illinois University  9/1/2010  6/10/2011 
Nusret Balci  University of Minnesota  9/1/2009  8/31/2011 
Wolfgang Bangerth  Texas A & M University  6/5/2011  6/10/2011 
Peter W. Bates  Michigan State University  6/19/2011  7/1/2011 
Mark Berliner  Ohio State University  6/7/2011  6/10/2011 
Robert Berry  Sandia National Laboratories  6/1/2011  6/4/2011 
Albert Boggess  Texas A & M University  5/31/2011  6/2/2011 
Andrew J. Booker  Boeing  6/1/2011  6/4/2011 
Olus N. Boratav  Corning Incorporated  6/5/2011  6/10/2011 
Joseph P. Brennan  University of Central Florida  5/31/2011  6/2/2011 
Susanne C. Brenner  Louisiana State University  9/1/2010  6/10/2011 
Corey Bryant  University of Texas at Austin  6/5/2011  6/10/2011 
Tan BuiThanh  University of Texas at Austin  6/5/2011  6/10/2011 
Vera Bulaevskaya  Lawrence Livermore National Laboratory  6/1/2011  6/4/2011 
John Burke  Boston University  6/19/2011  7/1/2011 
Leslie Button  Corning Incorporated  5/31/2011  6/4/2011 
Jeanine Buyck  University of Minnesota  6/20/2011  6/24/2011 
Greg Buzzard  Purdue University  6/1/2011  6/3/2011 
Julio Enrique Castrillon Candas  King Abdullah University of Science & Technology  6/5/2011  6/10/2011 
Aycil Cesmelioglu  University of Minnesota  9/30/2010  8/30/2012 
Chi Hin Chan  University of Minnesota  9/1/2009  8/31/2011 
Sousada Chidthachack  University of Minnesota  6/20/2011  6/24/2011 
Julianne Chung  University of Maryland  6/5/2011  6/10/2011 
Bernardo Cockburn  University of Minnesota  9/1/2010  6/30/2011 
Paul Constantine  Sandia National Laboratories  6/5/2011  6/10/2011 
Jintao Cui  University of Minnesota  8/31/2010  8/30/2012 
Tiangang Cui  University of Auckland  6/5/2011  6/11/2011 
Paul Davis  Worcester Polytechnic Institute  5/31/2011  6/2/2011 
Clint Dawson  University of Texas at Austin  6/5/2011  6/10/2011 
Rafael de la Llave  University of Texas at Austin  6/19/2011  7/1/2011 
Oliver R. DiazEspinosa  Duke University  6/19/2011  7/1/2011 
Andrew Dienstfrey  National Institute of Standards and Technology  5/31/2011  6/3/2011 
Tom Duchamp  University of Washington  4/1/2011  6/15/2011 
Louis J. Durlofsky  Stanford University  6/5/2011  6/8/2011 
Richard Dwight  Delft University of Technology  6/5/2011  6/10/2011 
Jens Lohne Eftang  Norwegian University of Science and Technology (NTNU)  6/4/2011  6/10/2011 
Virginie Ehrlacher  École des Ponts ParisTech  6/5/2011  6/10/2011 
Mohamed Sami ElBialy  University of Toledo  6/19/2011  7/2/2011 
Selim Esedoglu  University of Michigan  1/20/2011  6/10/2011 
Malena Espanol  California Institute of Technology  6/5/2011  6/10/2011 
Randy H. Ewoldt  University of Minnesota  9/1/2009  8/31/2011 
Fariba Fahroo  US Air Force Research Laboratory  6/5/2011  6/10/2011 
Weifu Fang  Wright State University  6/5/2011  6/10/2011 
Oscar E. Fernandez  University of Michigan  8/31/2010  8/30/2011 
Colin Fox  University of Otago  6/5/2011  6/10/2011 
Daniel Frohardt  Wayne State University  5/31/2011  6/2/2011 
Baskar Ganapathysubramanian  Iowa State University  6/5/2011  6/10/2011 
Roger G. Ghanem  University of Southern California  6/2/2011  6/9/2011 
Omar Ghattas  University of Texas at Austin  6/5/2011  6/10/2011 
Aditi Ghosh  Texas A & M University  6/4/2011  6/10/2011 
Nathan Louis Gibson  Oregon State University  6/4/2011  6/10/2011 
Albert B. Gilg  Siemens  5/31/2011  6/7/2011 
Jay Gopalakrishnan  University of Florida  9/1/2010  6/30/2011 
Genetha Anne Gray  Sandia National Laboratories  6/1/2011  6/3/2011 
Alexander Grigo  University of Toronto  6/19/2011  7/1/2011 
Shiyuan Gu  Louisiana State University  9/1/2010  6/30/2011 
Eldad Haber  University of British Columbia  6/5/2011  6/10/2011 
Amit Halder  Corning Incorporated  6/1/2011  6/4/2011 
Àlex Haro Provinciale  University of Barcelona  6/18/2011  7/1/2011 
Gurgen (Greg) Hayrapetyan  Michigan State University  6/19/2011  7/1/2011 
Christopher Heil  Georgia Institute of Technology  5/31/2011  6/2/2011 
Patrick Heimbach  Massachusetts Institute of Technology  6/5/2011  6/10/2011 
Matthias Heinkenschloss  Rice University  5/31/2011  6/1/2011 
David Higdon  Los Alamos National Laboratory  6/6/2011  6/9/2011 
Lior Horesh  IBM  6/5/2011  6/10/2011 
Ibrahim Hoteit  King Abdullah University of Science & Technology  6/4/2011  6/10/2011 
Yulia Hristova  University of Minnesota  9/1/2010  8/31/2012 
Gemma Huguet  Centre de Recerca Matemàtica  6/19/2011  7/1/2011 
Charles S. Jackson  University of Texas at Austin  6/1/2011  6/4/2011 
Farhad Jafari  University of Wyoming  5/29/2011  6/2/2011 
Jan Dirk Jansen  Delft University of Technology  6/5/2011  6/10/2011 
Bangti Jin  Texas A & M University  6/5/2011  6/11/2011 
Gardar Johannesson  Lawrence Livermore National Laboratory  6/1/2011  6/4/2011 
Michael S. Jolly  Indiana University  5/31/2011  6/2/2011 
Donald R. Jones  General Motors  6/1/2011  6/4/2011 
Sunnie Joshi  Texas A & M University  6/5/2011  6/10/2011 
Alex Kalmikov  Massachusetts Institute of Technology  6/4/2011  6/10/2011 
Markus Keel  University of Minnesota  7/21/2008  6/30/2011 
Stephen Keeler  Boeing  5/31/2011  6/2/2011 
Kimberly D. Kendricks  Central State University  6/5/2011  7/6/2011 
Gabor Kiss  University of Exeter  6/19/2011  7/1/2011 
Erica Zimmer Klampfl  Ford  5/31/2011  6/2/2011 
Hector Klie  ConocoPhillips  6/4/2011  6/12/2011 
Wolfgang Kliemann  Iowa State University  5/31/2011  6/1/2011 
Pawel Konieczny  University of Minnesota  9/1/2009  8/31/2011 
Drew Philip Kouri  Rice University  6/5/2011  6/11/2011 
Komandur R. Krishnan  Telcordia  5/31/2011  6/2/2011 
GuangTsai Lei  GTG Research  6/19/2011  7/1/2011 
GuangTsai Lei  GTG Research  6/5/2011  6/10/2011 
Suzanne Lenhart  University of Tennessee  5/31/2011  6/2/2011 
Gilad Lerman  University of Minnesota  9/1/2010  6/30/2011 
Pierre FJ Lermusiaux  Massachusetts Institute of Technology  6/5/2011  6/10/2011 
Mark Levi  Pennsylvania State University  5/31/2011  6/2/2011 
Dmitriy Leykekhman  University of Connecticut  6/5/2011  6/11/2011 
Hengguang Li  University of Minnesota  8/16/2010  8/15/2011 
Ji Li  Brigham Young University  6/19/2011  7/1/2011 
Zeng Lian  New York University  6/19/2011  7/2/2011 
Yu Liang  Michigan State University  6/19/2011  7/1/2011 
Guang Lin  Pacific Northwest National Laboratory  6/2/2011  6/4/2011 
Zhi (George) Lin  University of Minnesota  9/1/2009  8/31/2011 
Zhiwu Lin  Georgia Institute of Technology  6/19/2011  7/1/2011 
David Lindberg  Norwegian University of Science and Technology (NTNU)  6/4/2011  6/11/2011 
Jiangguo (James) Liu  Colorado State University  5/31/2011  6/2/2011 
Martin WenYu Lo  National Aeronautics and Space Administration (NASA)  6/19/2011  7/1/2011 
Quan Long  King Abdullah University of Science & Technology  6/5/2011  6/11/2011 
Vanessa LopezMarrero  IBM  6/5/2011  6/10/2011 
Nan Lu  Georgia Institute of Technology  6/19/2011  7/1/2011 
Christian Lucero  Colorado School of Mines  6/5/2011  6/10/2011 
Roger Lui  Worcester Polytechnic Institute  5/31/2011  6/2/2011 
Mitchell Luskin  University of Minnesota  9/1/2010  6/30/2011 
Suping Lyu  Medtronic  6/1/2011  6/1/2011 
Kara Lee Maki  University of Minnesota  9/1/2009  8/31/2011 
Bani K. Mallick  Texas A & M University  6/7/2011  6/10/2011 
Yu (David) Mao  University of Minnesota  8/31/2010  8/30/2012 
María Gabriela Martínez López  Stevens Institute of Technology  6/1/2011  6/4/2011 
Youssef Marzouk  Massachusetts Institute of Technology  6/5/2011  6/10/2011 
Jodi L. Mead  Boise State University  6/4/2011  6/7/2011 
Giovanni Migliorati  Politecnico di Milano  6/1/2011  6/11/2011 
Irina Mitrea  University of Minnesota  8/16/2010  6/24/2011 
Dimitrios Mitsotakis  University of Minnesota  10/27/2010  8/31/2012 
JoseMaria Mondelo  Autonomous University of Barcelona  6/19/2011  7/1/2011 
Charles Howard Morgan Jr.  Lock Haven University  6/19/2011  7/1/2011 
Jeff Morgan  University of Houston  5/31/2011  6/2/2011 
Rebecca Elizabeth Morrison  University of Texas at Austin  6/5/2011  6/10/2011 
April Marie Morton  California State Polytechnic University  6/2/2011  6/5/2011 
Benson Muite  University of Michigan  6/19/2011  7/1/2011 
Inge Myrseth  Norwegian Computing Center  6/5/2011  6/11/2011 
Geir Naevdal  International Research Institute of Stavanger  6/4/2011  6/10/2011 
Habib Najm  Sandia National Laboratories  6/5/2011  6/10/2011 
Michael Joseph Neilan  Louisiana State University  6/5/2011  6/10/2011 
Sylvain Nintcheu Fata  Oak Ridge National Laboratory  6/5/2011  6/11/2011 
Minah Oh  James Madison University  6/9/2011  6/11/2011 
Dianne P. O'Leary  University of Maryland  6/6/2011  6/10/2011 
Zubin Olikara  University of Colorado  6/19/2011  7/1/2011 
Dean S. Oliver  University of Bergen  6/4/2011  6/10/2011 
Norreen Olver  University of Minnesota  6/20/2011  6/24/2011 
Peter J. Olver  University of Minnesota  6/1/2011  6/1/2011 
Henning Omre  Norwegian University of Science and Technology (NTNU)  6/4/2011  6/12/2011 
Alexandra Ortan  University of Minnesota  6/20/2011  6/24/2011 
Alexandra Ortan  University of Minnesota  9/16/2010  6/15/2011 
Cecilia OrtizDuenas  University of Minnesota  9/1/2009  8/31/2011 
George C. Papanicolaou  Stanford University  6/1/2011  6/3/2011 
EunHee Park  Louisiana State University  6/4/2011  6/10/2011 
Abani Patra  University at Buffalo (SUNY)  6/5/2011  6/10/2011 
Bruce B. Peckham  University of Minnesota  6/19/2011  7/1/2011 
Malgorzata Peszynska  Oregon State University  6/2/2011  6/10/2011 
Nikola Petrov  University of Oklahoma  6/19/2011  7/1/2011 
Tuoc Van Phan  University of Tennessee  6/19/2011  7/1/2011 
Petr Plechac  University of Delaware  5/31/2011  6/4/2011 
Serge Preston  Portland State University  5/31/2011  6/2/2011 
Sridevi Pudipeddi  Waldorf College  5/1/2011  6/30/2011 
Roland Pulch  Bergische UniversitätGesamthochschule Wuppertal (BUGH)  6/1/2011  6/4/2011 
Weifeng (Frederick) Qiu  University of Minnesota  8/31/2010  8/30/2012 
Vincent QuennevilleBelair  University of Minnesota  9/16/2010  6/15/2011 
Katie Quertermous  James Madison University  6/17/2011  6/25/2011 
Wayne Raskind  Arizona State University  5/31/2011  6/2/2011 
Sivaguru S Ravindran  University of Alabama  6/1/2011  6/5/2011 
Fernando Reitich  University of Minnesota  9/1/2010  6/30/2011 
Rosemary Renaut  Arizona State University  6/5/2011  6/10/2011 
Juan Mario Restrepo  University of Arizona  6/5/2011  6/10/2011 
Kjartan Rimstad  Norwegian University of Science and Technology (NTNU)  6/4/2011  6/10/2011 
Werner Römisch  HumboldtUniversität  6/1/2011  6/5/2011 
Si Mohamed Sah  Duke University  6/19/2011  7/2/2011 
Julio Cesar Salazar Ospina  École Polytechnique de Montréal  6/19/2011  7/2/2011 
Tariq Samad  Honeywell  6/1/2011  6/1/2011 
Adrian Sandu  Virginia Polytechnic Institute and State University  6/5/2011  6/10/2011 
Fadil Santosa  University of Minnesota  7/1/2008  8/30/2011 
Stephen Schecter  North Carolina State University  6/19/2011  7/1/2011 
George R Sell  University of Minnesota  6/20/2011  7/1/2011 
Shuanglin Shao  University of Minnesota  9/1/2009  8/31/2011 
Paul Shearer  University of Michigan  6/5/2011  6/11/2011 
Zhongwei Shen  University of Kentucky  5/31/2011  6/2/2011 
Ratnasingham Shivaji  Mississippi State University  5/31/2011  6/2/2011 
Christine A. Shoemaker  Cornell University  6/5/2011  6/10/2011 
Gideon Simpson  University of Toronto  6/4/2011  6/10/2011 
Erkki Somersalo  Case Western Reserve University  6/5/2011  6/10/2011 
Richard Sowers  University of Illinois at UrbanaChampaign  5/31/2011  6/2/2011 
Milena Stanislavova  University of Kansas  6/19/2011  7/1/2011 
Panagiotis Stinis  University of Minnesota  9/1/2010  6/30/2011 
Allan Struthers  Michigan Technological University  5/31/2011  6/2/2011 
Liyeng Sung  Louisiana State University  9/1/2010  6/10/2011 
Laura Swiler  Sandia National Laboratories  6/1/2011  6/5/2011 
Adama Tandia  Corning Incorporated  6/1/2011  6/11/2011 
Nicolae Tarfulea  Purdue University, Calumet  9/1/2010  6/15/2011 
Nicoleta Eugenia Tarfulea  Purdue University, Calumet  6/5/2011  6/10/2011 
Daniel M. Tartakovsky  University of California, San Diego  6/5/2011  6/9/2011 
Luis Tenorio  Colorado School of Mines  3/27/2011  6/12/2011 
Gabriel Alin Terejanu  University of Texas at Austin  6/1/2011  6/4/2011 
Carlos Alberto Trenado  University of Maryland  6/5/2011  6/10/2011 
Dimitar Trenev  University of Minnesota  9/1/2009  8/31/2011 
Bart van Bloemen Waanders  Sandia National Laboratories  6/5/2011  6/10/2011 
Jin Wang  Old Dominion University  6/19/2011  6/30/2011 
Liping Wang  General Electric  6/1/2011  6/4/2011 
Utz Wever  Siemens  6/1/2011  6/11/2011 
Klaus D. Wiegand  ExxonMobil  5/31/2011  6/2/2011 
Karen E. Willcox  Massachusetts Institute of Technology  6/5/2011  6/10/2011 
Chai Wah Wu  IBM  5/31/2011  6/2/2011 
Alexander Wurm  Western New England College  6/19/2011  7/1/2011 
Zhifu Xie  Virginia State University  6/19/2011  7/1/2011 
Dongbin Xiu  Purdue University  6/1/2011  6/4/2011 
Lingzhou Xue  University of Minnesota  6/6/2011  6/11/2011 
Lingzhou Xue  University of Minnesota  6/2/2011  6/4/2011 
Yangbo Ye  University of Iowa  5/31/2011  6/1/2011 
Feng Yi  University of Minnesota  6/6/2011  6/10/2011 
Ganghua Yuan  Northeast (Dongbei) Normal University  4/27/2011  7/27/2011 
Chunfeng Zhou  Corning Incorporated  6/1/2011  6/4/2011 
Zhengfang Zhou  Michigan State University  5/31/2011  6/2/2011 