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IMA Newsletter #416

June 2011

2010-2011 Program

See http://www.ima.umn.edu/2010-2011/ for a full description of the 2010-2011 program on Simulating Our Complex World: Modeling, Computation and Analysis.

IMA Events

IMA Workshop

Uncertainty Quantification in Industrial and Energy Applications: Experiences and Challenges

June 2-4, 2011

Organizers: Albert B. Gilg (Siemens AG), Laura Swiler (Sandia National Laboratories)

IMA Tutorial

Large-scale Inverse Problems and Quantification of Uncertainty

June 5, 2011

Organizers: Clint Dawson (University of Texas), Luis Tenorio (Colorado School of Mines)

IMA Annual Program Year Workshop

Large-scale Inverse Problems and Quantification of Uncertainty

June 6-10, 2011

Organizers: Clint Dawson (University of Texas), Omar Ghattas (University of Texas), Luis Tenorio (Colorado School of Mines), Karen E. Willcox (Massachusetts Institute of Technology)

Girls and Mathematics Summer Day Program

June 20-24, 2011

Organizers: Irina Mitrea (University of Minnesota Twin Cities), Katie Quertermous (James Madison University)

Invariant Objects in Dynamical Systems and their Applications

June 20 - July 1, 2011

Organizers: Peter W. Bates (Michigan State University), Rafael de la Llave (University of Texas)
Schedule

Wednesday, June 1

2:30pm-3:00pm Coffee BreakLind Hall 400

Thursday, June 2

8:00am-8:30am Registration and coffeeLind Hall 400 SW6.2-4.11
8:30am-8:45pm Welcome to the IMAFadil Santosa (University of Minnesota)Lind Hall 305 SW6.2-4.11
8:45am-9:45am Challenges In Uncertainty, Calibration, Validation and Predictability of Engineering Analysis ModelsLiping Wang (General Electric)Lind Hall 305 SW6.2-4.11
9:45am-10:45am Scientific and statistical challenges to quantifying uncertainties in climate projectionsCharles S. Jackson (University of Texas at Austin)Lind Hall 305 SW6.2-4.11
10:45am-11:15am Coffee breakLind Hall 400 SW6.2-4.11
11:15am-12:15pm Gradient-Enhanced Uncertainty PropagationMihai Anitescu (Argonne National Laboratory)Lind Hall 305 SW6.2-4.11
12:15pm-1:45pm Lunch SW6.2-4.11
1:45pm-2:45pm Multiple Model Inference: Calibration and Selection with Multiple ModelsLaura Swiler (Sandia National Laboratories)Lind Hall 305 SW6.2-4.11
3:00pm-4:00pm Improved Quantification of Prediction Error for Kriging Response SurfacesDonald R. Jones (General Motors)Lind Hall 305 SW6.2-4.11
4:00pm-6:00pm Reception and Poster Session
Poster submissions welcome from all participants
Lind Hall 400 SW6.2-4.11
Poster -Algorithm Class ARODEFlorian Augustin (TU München)
Poster- Robust Design for Industrial ApplicationsAlbert B. Gilg (Siemens)
Utz Wever (Siemens)
Poster - Scientific and statistical challenges to quantifying uncertainties in climate projectionsCharles S. Jackson (University of Texas at Austin)
Poster- The Uncertainty Quantification Project at Lawrence Livermore National Laboratory: Sensitivities and Uncertainties of the Community Atmosphere ModelGardar Johannesson (Lawrence Livermore National Laboratory)
Poster - Error Reduction and Optimal Parameters Estimation in Convective Cloud Scheme in Climate ModelGuang Lin (Pacific Northwest National Laboratory)
Poster- Stochastic Two-Stage Problems with Stochastic Dominance ConstraintMaría Gabriela Martínez López (Stevens Institute of Technology)
Poster - Polynomial Chaos for Differential Algebraic Equations with Random ParametersRoland Pulch (Bergische Universität-Gesamthochschule Wuppertal (BUGH))
Poster- An Information Theoretic Approach to Model Calibration and Validation using QUESOGabriel Alin Terejanu (University of Texas at Austin)

Friday, June 3

All Day Morning Session Chair: Roger Ghanem (University of Souther California)
All Day Morning Session Chair: Roger G. Ghanem (University of Southern California) SW6.2-4.11
8:30am-9:00am CoffeeLind Hall 400 SW6.2-4.11
9:00am-10:00am Scenario generation in stochastic programming with application to optimizing electricity portfolios under uncertaintyWerner Römisch (Humboldt-Universität)Lind Hall 305 SW6.2-4.11
10:00am-11:00am Uncertainty quantification of shock interactions with complex environmentsGeorge C. Papanicolaou (Stanford University)Lind Hall 305 SW6.2-4.11
11:00am-12:00pm Discussion SessionLind Hall 305 SW6.2-4.11
12:00pm-1:00pm Lunch SW6.2-4.11
1:00pm-2:00pm Mastering Impact of Uncertainties by Robust Design Optimization Techniques for Turbo-MachineryAlbert B. Gilg (Siemens)Lind Hall 305 SW6.2-4.11
2:00pm-3:00pm Efficient UQ algorithms for practical systemsDongbin Xiu (Purdue University)Lind Hall 305 SW6.2-4.11
3:00pm-3:15pm Group Photo SW6.2-4.11
3:15pm-3:30pm Coffee breakLind Hall 400 SW6.2-4.11
3:30pm-4:30pm The Curse of Dimensionality, Model Validation, and UQ.Roger G. Ghanem (University of Southern California)Lind Hall 305 SW6.2-4.11
4:30pm-5:30pm Discussion SessionLind Hall 305 SW6.2-4.11
6:00pm-8:30pm Social Hour at the Campus Club - Coffman Memorial Union300 Washington Avenue SEMinneapolis MN 55455 SW6.2-4.11

Saturday, June 4

8:30am-9:00am CoffeeLind Hall 400 SW6.2-4.11
9:00am-10:00am Uncertainty Quantification and Optimization Under Uncertainty: Experience and ChallengesAndrew J. Booker (Boeing)Lind Hall 305 SW6.2-4.11
10:00am-11:00am Design For Variation at Pratt & WhitneyGrant Reinman (Pratt & Whitney)Lind Hall 305 SW6.2-4.11
11:00am-12:00pm Final Discussion SessionLind Hall 305 SW6.2-4.11

Sunday, June 5

8:30am-9:00am Registration and coffee Lind Hall 400 T6.5.11
9:00am-10:30am TutorialLuis Tenorio (Colorado School of Mines)Lind Hall 305 T6.5.11
10:30am-11:00am Coffee breakLind Hall 400 T6.5.11
11:00am-12:30pm Tutorial (continued)Luis Tenorio (Colorado School of Mines)Lind Hall 305 T6.5.11
12:30pm-2:00pm Lunch T6.5.11
2:00pm-3:30pm TutorialYoussef Marzouk (Massachusetts Institute of Technology)Lind Hall 305 T6.5.11
3:30pm-4:00pm Coffee breakLind Hall 400 T6.5.11
4:00pm-5:30pm Tutorial (continued)Youssef Marzouk (Massachusetts Institute of Technology)Lind Hall 305 T6.5.11

Monday, June 6

All Day Chairs: Omar Ghattas (University of Texas at Austin) and Karen E. Willcox (Massachusetts Institute of Technology) W6.6-10.11
9:00am-9:30am Registration and coffee Keller Hall 3-176 W6.6-10.11
9:30am-9:45am Welcome to the IMAFadil Santosa (University of Minnesota)Keller Hall 3-180 W6.6-10.11
9:45am-10:30am Introduction blitz by participantsKeller Hall 3-180 W6.6-10.11
10:30am-11:00am Coffee breakKeller Hall 3-176 W6.6-10.11
11:00am-12:00pm Workshop IntroductionOmar Ghattas (University of Texas at Austin)
Karen E. Willcox (Massachusetts Institute of Technology)
Keller Hall 3-180 W6.6-10.11
12:00pm-1:45pm Lunch W6.6-10.11
1:45pm-2:45pm The best we can do with MCMC, and how to do better.Colin Fox (University of Otago)Keller Hall 3-180 W6.6-10.11
2:45pm-3:00pm Coffee breakKeller Hall 3-176 W6.6-10.11
3:00pm-4:00pm Confidence in Image ReconstructionDianne P. O'Leary (University of Maryland)Keller Hall 3-180 W6.6-10.11
4:00pm-4:15pm Group photo W6.6-10.11

Tuesday, June 7

All Day Chairs: Luis Tenorio (Colorado School of Mines) and Eldad Haber (University of British Columbia) W6.6-10.11
8:30am-9:00am CoffeeKeller Hall 3-176 W6.6-10.11
9:00am-10:00am System-theoretical aspects of oil and gas reservoir history matchingJan Dirk Jansen (Delft University of Technology)Keller Hall 3-180 W6.6-10.11
10:00am-10:30am Coffee breakKeller Hall 3-176 W6.6-10.11
10:30am-11:30am Spatial categorical inversion: Seismic inversion into lithology/fluid classesHenning Omre (Norwegian University of Science and Technology (NTNU))Keller Hall 3-180 W6.6-10.11
11:30am-1:00pm Lunch W6.6-10.11
1:00pm-2:00pm Ensemble-based methods: filters, smoothers and iterationDean S. Oliver (University of Bergen)Keller Hall 3-180 W6.6-10.11
2:00pm-2:30pm Coffee breakKeller Hall 3-176 W6.6-10.11
2:30pm-3:30pm Ocean Uncertainty Prediction and non-Gaussian Data Assimilation with Stochastic PDEs: Bye-Bye Monte-Carlo?Pierre FJ Lermusiaux (Massachusetts Institute of Technology)Keller Hall 3-180 W6.6-10.11
3:30pm-5:30pm Reception and Poster Session
Poster submissions welcome from all participants
Instructions
Lind Hall 400 W6.6-10.11
Poster- Detecting small low emission radiating sourcesMoritz Allmaras (Texas A & M University)
Yulia Hristova (University of Minnesota)
Poster - Scalable parallel algorithms for uncertainty quantification in high dimensional inverse problemsTan Bui-Thanh (University of Texas at Austin)
Poster- Designing Optimal Spectral Filters for Inverse ProblemsJulianne Chung (University of Maryland)
Poster- Bayesian Inference for Data Assimilation using Least-Squares Finite Element MethodsRichard Dwight (Delft University of Technology)
Poster - Convergence of a greedy algorithm for high-dimensional convex nonlinear problems Virginie Ehrlacher (École des Ponts ParisTech)
Poster- Robust Design for Industrial ApplicationsAlbert B. Gilg (Siemens)
Utz Wever (Siemens)
Poster - Sparsity reconstruction in electrical impedance tomographyBangti Jin (Texas A & M University)
Poster- A Multiscale Learning Approach for History MatchingHector Klie (ConocoPhillips)
Poster- Information Gain in Model Validation for Porous MediaQuan Long King Abdullah University of Science & Technology, University of Texas at Austin
Poster-A hybrid numerical method for the numerical solution of the Benjamin equationDimitrios Mitsotakis (University of Minnesota)
Poster- Modeling and Analysis of HIV Evolution and TherapyNicolae Tarfulea (Purdue University, Calumet)

Wednesday, June 8

All Day Chairs: Omar Ghattas (University of Texas at Austin) and Luis Tenorio (Colorado School of Mines) W6.6-10.11
8:30am-9:00am CoffeeKeller Hall 3-176 W6.6-10.11
9:00am-10:00am Design of simultaneous sourceEldad Haber (University of British Columbia)Keller Hall 3-180 W6.6-10.11
10:00am-10:30am Coffee breakKeller Hall 3-176 W6.6-10.11
10:30am-11:30am Data Assimilation and Efficient Forward Modeling for Subsurface FlowLouis J. Durlofsky (Stanford University)Keller Hall 3-180 W6.6-10.11
11:30am-1:00pm Lunch W6.6-10.11
1:00pm-2:00pm Bayesian approaches for combining computational model output and physical observationsDavid Higdon (Los Alamos National Laboratory)Keller Hall 3-180 W6.6-10.11
2:00pm-2:30pm Coffee breakKeller Hall 3-176 W6.6-10.11
2:30pm-3:30pm A map-based approach to Bayesian inference in inverse problemsYoussef Marzouk (Massachusetts Institute of Technology)Keller Hall 3-180 W6.6-10.11
3:30pm-3:45pm Coffee breakKeller Hall 3-176 W6.6-10.11
3:45pm-4:15pm NSF SEES PresentationRosemary Renaut (Arizona State University)Keller Hall 3-180 W6.6-10.11
4:15pm-7:00pm Social event at Buffalo Wild WingsBuffalo Wild Wings at Station 19 - 2001 SE University Avenue Suite 100, Minneapolis, MN 55455-2195 Phone: 612-617-9464 W6.6-10.11

Thursday, June 9

All Day Chairs: Karen E. Willcox (Massachusetts Institute of Technology) and Clint N. Dawson (University of Texas at Austin) W6.6-10.11
8:30am-9:00am CoffeeKeller Hall 3-176 W6.6-10.11
9:00am-10:00am Hierarchical Bayesian Models for Uncertainty Quantification and Model ValidationRoger G. Ghanem (University of Southern California)Keller Hall 3-180 W6.6-10.11
10:00am-10:30am Coffee breakKeller Hall 3-176 W6.6-10.11
10:30am-11:30am DiscussionKeller Hall 3-180 W6.6-10.11
11:30am-1:00pm Lunch W6.6-10.11
1:00pm-2:00pm An approach for robust segmentation of images from arbitrary Fourier data using l1 minimization techniquesRosemary Renaut (Arizona State University)Keller Hall 3-180 W6.6-10.11
2:00pm-2:30pm Coffee breakKeller Hall 3-176 W6.6-10.11
2:30pm-3:30pm Bayesian Uncertainty Quantification for Subsurface Inversion using Multiscale Hierarchical ModelBani K. Mallick (Texas A & M University)Keller Hall 3-180 W6.6-10.11
3:30pm-4:00pm Coffee breakKeller Hall 3-176 W6.6-10.11
4:00pm-5:00pm Climate Variability: Goals and ChallengesJuan Mario Restrepo (University of Arizona)Keller Hall 3-180 W6.6-10.11

Friday, June 10

All Day Chair: Omar Ghattas (University of Texas at Austin) W6.6-10.11
8:00am-8:30am CoffeeKeller Hall 3-176 W6.6-10.11
8:30am-9:30am Hierarchical Bayesian Modeling: Why and HowMark Berliner (Ohio State University)Keller Hall 3-180 W6.6-10.11
9:30am-9:45am Coffee breakKeller Hall 3-176 W6.6-10.11
9:45am-10:45am Surrogate Response Surfaces in Global Optimization and Uncertainty Quantification of Computationally Expensive Simulations with PDE and Environmental Inverse ApplicationsChristine A. Shoemaker (Cornell University)Keller Hall 3-180 W6.6-10.11
10:45am-11:00am Coffee breakKeller Hall 3-176 W6.6-10.11
11:00am-12:00pm Efficient estimates of prior information and uncertainty with chi-square testsJodi L. Mead ()Keller Hall 3-180 W6.6-10.11
12:00pm-12:05pm Closing remarksKeller Hall 3-180 W6.6-10.11

Monday, June 13

2:30pm-3:00pm Coffee BreakLind Hall 400

Wednesday, June 15

2:30pm-3:00pm Coffee BreakLind Hall 400

Thursday, June 16

2:30pm-3:00pm Coffee BreakLind Hall 400

Friday, June 17

2:30pm-3:00pm Coffee breakLind Hall 400

Monday, June 20

9:00am-10:30am Lecture 1Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Lecture 1 - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch ND6.20-7.1.11
2:00pm-3:30pm Lecture 2Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400
4:00pm-5:30pm Lecture 1Àlex Haro Provinciale (University of Barcelona)Lind Hall 305 ND6.20-7.1.11

Tuesday, June 21

9:00am-10:30am Lecture 2 - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Lecture 3Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch ND6.20-7.1.11
2:00pm-3:30pm Lecture 3 - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400
4:00pm-5:30pm TBAGeorge R Sell (University of Minnesota)Lind Hall 305 ND6.20-7.1.11

Wednesday, June 22

9:00am-10:30am Lecture 4Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Lecture 4 - - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch ND6.20-7.1.11
2:00pm-5:30pm Afternoon Free ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400

Thursday, June 23

9:00am-10:30am Lecture 5Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Lecture 5 - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch ND6.20-7.1.11
2:00pm-3:30pm Lecture 6Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400
4:00pm-5:30pm Huguet-Lecture 1Gemma Huguet (Centre de Recerca Matemàtica )Lind Hall 305 ND6.20-7.1.11

Friday, June 24

9:00am-10:30am Lecture 6 - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Lecture 7Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch ND6.20-7.1.11
2:00pm-3:30pm Lecture 7 - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400
4:00pm-5:30pm Lecture 2Àlex Haro Provinciale (University of Barcelona)Lind Hall 305 ND6.20-7.1.11

Monday, June 27

9:00am-10:30am Lecture 8Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Lecture 8 - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch ND6.20-7.1.11
2:00pm-3:30pm Lecture 2Gemma Huguet (Centre de Recerca Matemàtica )Lind Hall 305 ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400
4:00pm-5:30pm Lecture 3Àlex Haro Provinciale (University of Barcelona)Lind Hall 305 ND6.20-7.1.11

Tuesday, June 28

9:00am-10:30am Lecture 9Rafael de la Llave (University of Texas at Austin)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Exchange lemmasStephen Schecter (North Carolina State University)Lind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch ND6.20-7.1.11
2:00pm-3:30pm Lecture 3Gemma Huguet (Centre de Recerca Matemàtica )Lind Hall 305 ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400
4:00pm-5:30pm Lecture 1Martin Wen-Yu Lo (National Aeronautics and Space Administration (NASA))Lind Hall 305 ND6.20-7.1.11

Wednesday, June 29

9:00am-10:30am Lecture 9 - Normally Hyperbolic Invariant Manifolds: Existence, Persistence, Approximation, and Their ApplicationsPeter W. Bates (Michigan State University)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Lecture 2Martin Wen-Yu Lo (National Aeronautics and Space Administration (NASA))Lind Hall 305 ND6.20-7.1.11
12:30pm-5:30pm Afternoon Free ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400

Thursday, June 30

9:00am-10:30am Loss of normal hyperbolicityStephen Schecter (North Carolina State University)Lind Hall 305 ND6.20-7.1.11
11:00am-12:30pm TBAZeng Lian (New York University)Lind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch ND6.20-7.1.11
2:00pm-3:30pm Other attendees speakLind Hall 305 ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400
4:00pm-5:30pm Plus open problemsLind Hall 305 ND6.20-7.1.11

Friday, July 1

9:00am-10:30am Other attendees speakLind Hall 305 ND6.20-7.1.11
11:00am-12:30pm Plus open problemsLind Hall 305 ND6.20-7.1.11
12:30pm-2:00pm Lunch Break ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400
4:00pm-5:30pm departLind Hall 305 ND6.20-7.1.11
Abstracts
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 Compton-type measurements in 2D and 3D.
Mihai Anitescu (Argonne National Laboratory) Gradient-Enhanced 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 derivative-free 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. 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.
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 Ice-Stream in Greenland. The next portion of the talk presents ideas and examples for incorporating output from large-scale 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 Bui-Thanh (University of Texas at Austin) Poster - Scalable parallel algorithms for uncertainty quantification in high dimensional inverse problems
Abstract: Quantifying uncertainties in large-scale 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 large-scale 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 Markov-chain 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 expensive-to-solve 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 PDE-like 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 matrix-free Lanczos iterations, which typically requires a dimension-independent 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 ill-posed 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 pre-compute 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 well-established 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 real-time model-based 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 reduced-order models for efficient flow simulations will be described. KPCA-based representations will be shown to better capture multipoint spatial statistics, which gives them an advantage over standard Karhunen-Loeve procedures for representing complex geological systems. The use of KPCA within a gradient-based data assimilation (history matching) procedure will be illustrated. Next, a reduced-order 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 low-dimensional subspace using proper orthogonal decomposition. The method requires training runs that are performed using a full-order 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 Least-Squares Finite Element Methods
Abstract: It has recently been observed that Least-Squares Finite Element methods (LS-FEMs) 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 Navier-Stokes 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 high-dimensional 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: Sample-based inference is a great way to summarize inverse and predictive distributions arising in large-scale 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 largest-scale 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 low-level mid-level and high-level 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 low-dimensional 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 data-poor 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 specimen-to-specimen variability and also in terms of variability in their subscale features. The ability of these doubly-stochastic expansions to improve the predictive value of model-based 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 Large-scale 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 Turbo-Machinery
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. Time-consuming 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 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.
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 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.
Jan Dirk Jansen (Delft University of Technology) System-theoretical 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 ill-posed 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 well-posedness 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 kriging-based 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 non-Gaussian Data Assimilation with Stochastic PDEs: Bye-Bye Monte-Carlo?
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 time-dependent ocean and fluid flows in two spatial dimensions.

Co-authors 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. Karhunen-Lo'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 well-posed 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 two-stage 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 Two-Stage Problems with Stochastic Dominance Constraint
Abstract: 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.
Youssef Marzouk (Massachusetts Institute of Technology) A map-based approach to Bayesian inference in inverse problems
Abstract: Bayesian inference provides a natural framework for quantifying uncertainty in PDE-constrained 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 chain-based 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 chi-square tests
Abstract: Many practical inverse problems are ill-posed, 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 large-scale, ill-posed 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 chi-square tests to calculate the second moment of the error in a model, an initial parameter estimate, or data. Efficient newton-type algorithms have been developed to calculate regularization parameters, and estimate the standard deviation of data error. More recently, we have used chi-square tests to calculate diagonal error covariance matrices and these can be used to obtain non-smooth least squares solutions. Finally, we have developed the chi-squared method for nonlinear problems and will show some recent results. Applications with the chi-square method includes soil moisture estimation, lagrangian flow, and threat detection.
Dimitrios Mitsotakis (University of Minnesota) Poster-A 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 two-stage Gauss–Legendre implicit Runge-Kutta 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 ill-posed. By definition, small changes in the data to an ill-posed 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) Ensemble-based methods: filters, smoothers and iteration
Abstract: For many large-scale nonlinear inverse problems, Monte Carlo methods provide the only practical method of quantifying uncertainty. Ensemble-based 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 Gauss-Newton minimization methods and the approach to sampling. The methodology will be illustrated with several fairly large-scale 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 three-dimensional 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, angle-dependent 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 McMC-algorithm.

The methodology is defined and demonstrated on observations from a real North Sea reservoir.

Co-author: 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 Universität-Gesamthochschule Wuppertal (BUGH)) Poster - Polynomial Chaos for Differential Algebraic Equations with Random Parameters
Abstract: 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.
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 (Humboldt-Universität) Scenario generation in stochastic programming with application to optimizing electricity portfolios under uncertainty
Abstract: We review some recent advances in high-dimensional numerical integration, namely, in (i) optimal quantization of probability distributions, (ii) Quasi-Monte 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 risk-averse 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 risk-neutral and risk-averse situation, discuss the costs of risk aversion and provide several possibilities for risk aversion by multi-period 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 derivative-free 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 site-specific 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 sum-of-squared error function. Information theoretic approaches balance overfitting with model accuracy by incorporating terms that penalize more parameters with a log-likelihood 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 log-likelihood 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 drug-resistant virus variances. Thus, by including two viral strains, wild-type and drug-resistant, we show that the inclusion of the CTL compartment produces a higher rebound for an individual’s healthy helper T-cell 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 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 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.
Visitors in Residence
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 Bui-Thanh 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. Diaz-Espinosa 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
Guang-Tsai Lei GTG Research 6/19/2011 - 7/1/2011
Guang-Tsai 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 Wen-Yu 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 Lopez-Marrero 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
Jose-Maria 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 Ortiz-Duenas University of Minnesota 9/1/2009 - 8/31/2011
George C. Papanicolaou Stanford University 6/1/2011 - 6/3/2011
Eun-Hee 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 Universität-Gesamthochschule Wuppertal (BUGH) 6/1/2011 - 6/4/2011
Weifeng (Frederick) Qiu University of Minnesota 8/31/2010 - 8/30/2012
Vincent Quenneville-Belair 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 Humboldt-Universität 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 Urbana-Champaign 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
Li-yeng 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
Legend: Postdoc or Industrial Postdoc Long-term Visitor

IMA Affiliates:
Arizona State University, Boeing, Colorado State University, Corning Incorporated, ExxonMobil, Ford, General Motors, Georgia Institute of Technology, Honeywell, IBM, Indiana University, Iowa State University, Korea Advanced Institute of Science and Technology (KAIST), Lawrence Livermore National Laboratory, Lockheed Martin, Los Alamos National Laboratory, Medtronic, Michigan State University, Michigan Technological University, Mississippi State University, Northern Illinois University, Ohio State University, Pennsylvania State University, Portland State University, Purdue University, Rice University, Rutgers University, Sandia National Laboratories, Schlumberger Cambridge Research, Schlumberger-Doll, Seoul National University, Siemens, Telcordia, Texas A & M University, University of Central Florida, University of Chicago, University of Delaware, University of Houston, University of Illinois at Urbana-Champaign, University of Iowa, University of Kentucky, University of Maryland, University of Michigan, University of Minnesota, University of Notre Dame, University of Pennsylvania, University of Pittsburgh, University of Tennessee, University of Wisconsin-Madison, University of Wyoming, US Air Force Research Laboratory, Wayne State University, Worcester Polytechnic Institute