Reception and Poster Session <br><br/><br/>Poster submissions welcome from all participants
Thursday, June 2, 2011 - 4:00pm - 6:00pm
- Poster- Robust Design for Industrial Applications
Albert Gilg (Siemens AG)Utz Wever (Siemens AG)
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.
- Poster- The Uncertainty Quantification Project at Lawrence Livermore
National Laboratory: Sensitivities and Uncertainties of the Community
Gardar Johannesson (Lawrence Livermore National Laboratory)
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.
- Poster- An Information Theoretic Approach to Model Calibration and
Validation using QUESO
Gabriel Terejanu (The University of Texas at Austin)
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 k-nearest 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
- Poster - Scientific and statistical challenges to quantifying uncertainties in climate projections
Charles Jackson (The University of Texas at Austin)
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 log-likelihood. I have interpreted these differences to the existence of model biases and the potential that the minimum log-likelihood, which are often the desired solutions to data inversion problems, are over-fitting the data. Such a result highlights the need for a combination of scientific and computational thinking to begin to address uncertainties for complex multi-physics phenomena.
- Poster - Error Reduction and Optimal Parameters Estimation in Convective Cloud Scheme in Climate Model
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.
- Poster- Stochastic Two-Stage Problems with Stochastic Dominance Constraint
Gabriela Martínez (Stevens Institute of Technology)
We analyze stochastic two-stage 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 two-stage problems and construct successive approximations of the stochastic dominance constraint.
- Poster - Polynomial Chaos for Differential Algebraic Equations with Random Parameters
Roland Pulch (Bergische Universität-Gesamthochschule Wuppertal (BUGH))
Mathematical modeling of industrial applications often yields time-dependent
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.
- Poster -Algorithm Class ARODE
Florian Augustin (TU München)
Ordinary differential equations with uncertain parameters are a vast field of research.
Monte-Carlo 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 element-wise approach can be proven to converge. This poster presents
an algorithm, which is based on the stochastic Galerkin Runge-Kutta method.
It incorporates adaptive stepsize control in time and adaptive partitioning of
the parameter domain.