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Email: ima-staff@ima.umn.edu |
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Additional newsletters available at http://www.ima.umn.edu/newsletters
Additional newsletters available at http://www.ima.umn.edu/newsletters
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 Break | Lind Hall 400 |
Thursday, June 2
| 8:00am-8:30am | Registration and coffee | Lind Hall 400 | SW6.2-4.11 | |
| 8:30am-8:45pm | Welcome to the IMA | Fadil 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 Models | Liping Wang (General Electric) | Lind Hall 305 | SW6.2-4.11 |
| 9:45am-10:45am | Scientific and statistical challenges to quantifying uncertainties in climate projections | Charles S. Jackson (University of Texas at Austin) | Lind Hall 305 | SW6.2-4.11 |
| 10:45am-11:15am | Coffee break | Lind Hall 400 | SW6.2-4.11 | |
| 11:15am-12:15pm | Gradient-Enhanced Uncertainty Propagation | Mihai 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 Models | Laura Swiler (Sandia National Laboratories) | Lind Hall 305 | SW6.2-4.11 |
| 3:00pm-4:00pm | Improved Quantification of Prediction Error for Kriging Response Surfaces | Donald 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 ARODE | Florian Augustin (TU München) | |||
| Poster- Robust Design for Industrial Applications | Albert B. Gilg (Siemens) Utz Wever (Siemens) | |||
| Poster - Scientific and statistical challenges to quantifying uncertainties in climate projections | Charles S. Jackson (University of Texas at Austin) | |||
| Poster- The Uncertainty Quantification Project at Lawrence Livermore National Laboratory: Sensitivities and Uncertainties of the Community Atmosphere Model | Gardar Johannesson (Lawrence Livermore National Laboratory) | |||
| Poster - Error Reduction and Optimal Parameters Estimation in Convective Cloud Scheme in Climate Model | Guang Lin (Pacific Northwest National Laboratory) | |||
| Poster- Stochastic Two-Stage Problems with Stochastic Dominance Constraint | María Gabriela Martínez López (Stevens Institute of Technology) | |||
| Poster - Polynomial Chaos for Differential Algebraic Equations with Random Parameters | Roland Pulch (Bergische Universität-Gesamthochschule Wuppertal (BUGH)) | |||
| Poster- An Information Theoretic Approach to Model Calibration and Validation using QUESO | Gabriel 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 | Coffee | Lind Hall 400 | SW6.2-4.11 | |
| 9:00am-10:00am | Scenario generation in stochastic programming with application to optimizing electricity portfolios under uncertainty | Werner Römisch (Humboldt-Universität) | Lind Hall 305 | SW6.2-4.11 |
| 10:00am-11:00am | Uncertainty quantification of shock interactions with complex environments | George C. Papanicolaou (Stanford University) | Lind Hall 305 | SW6.2-4.11 |
| 11:00am-12:00pm | Discussion Session | Lind 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-Machinery | Albert B. Gilg (Siemens) | Lind Hall 305 | SW6.2-4.11 |
| 2:00pm-3:00pm | Efficient UQ algorithms for practical systems | Dongbin 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 break | Lind 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 Session | Lind Hall 305 | SW6.2-4.11 | |
| 6:00pm-8:30pm | Social Hour at the Campus Club - Coffman Memorial Union | 300 Washington Avenue SEMinneapolis MN 55455 | SW6.2-4.11 |
Saturday, June 4
| 8:30am-9:00am | Coffee | Lind Hall 400 | SW6.2-4.11 | |
| 9:00am-10:00am | Uncertainty Quantification and Optimization Under Uncertainty: Experience and Challenges | Andrew J. Booker (Boeing) | Lind Hall 305 | SW6.2-4.11 |
| 10:00am-11:00am | Design For Variation at Pratt & Whitney | Grant Reinman (Pratt & Whitney) | Lind Hall 305 | SW6.2-4.11 |
| 11:00am-12:00pm | Final Discussion Session | Lind 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 | Tutorial | Luis Tenorio (Colorado School of Mines) | Lind Hall 305 | T6.5.11 |
| 10:30am-11:00am | Coffee break | Lind 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 | Tutorial | Youssef Marzouk (Massachusetts Institute of Technology) | Lind Hall 305 | T6.5.11 |
| 3:30pm-4:00pm | Coffee break | Lind 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 IMA | Fadil Santosa (University of Minnesota) | Keller Hall 3-180 | W6.6-10.11 |
| 9:45am-10:30am | Introduction blitz by participants | Keller Hall 3-180 | W6.6-10.11 | |
| 10:30am-11:00am | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 11:00am-12:00pm | Workshop Introduction | Omar 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 break | Keller Hall 3-176 | W6.6-10.11 | |
| 3:00pm-4:00pm | Confidence in Image Reconstruction | Dianne 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 | Coffee | Keller Hall 3-176 | W6.6-10.11 | |
| 9:00am-10:00am | System-theoretical aspects of oil and gas reservoir history matching | Jan Dirk Jansen (Delft University of Technology) | Keller Hall 3-180 | W6.6-10.11 |
| 10:00am-10:30am | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 10:30am-11:30am | Spatial categorical inversion: Seismic inversion into lithology/fluid classes | Henning 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 iteration | Dean S. Oliver (University of Bergen) | Keller Hall 3-180 | W6.6-10.11 |
| 2:00pm-2:30pm | Coffee break | Keller 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 sources | Moritz Allmaras (Texas A & M University) Yulia Hristova (University of Minnesota) | |||
| Poster - Scalable parallel algorithms for uncertainty quantification in high dimensional inverse problems | Tan Bui-Thanh (University of Texas at Austin) | |||
| Poster- Designing Optimal Spectral Filters for Inverse Problems | Julianne Chung (University of Maryland) | |||
| Poster- Bayesian Inference for Data Assimilation using Least-Squares Finite Element Methods | Richard 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 Applications | Albert B. Gilg (Siemens) Utz Wever (Siemens) | |||
| Poster - Sparsity reconstruction in electrical impedance tomography | Bangti Jin (Texas A & M University) | |||
| Poster- A Multiscale Learning Approach for History Matching | Hector Klie (ConocoPhillips) | |||
| Poster- Information Gain in Model Validation for Porous Media | Quan Long King Abdullah University of Science & Technology, University of Texas at Austin | |||
| Poster-A hybrid numerical method for the numerical solution of the Benjamin equation | Dimitrios Mitsotakis (University of Minnesota) | |||
| Poster- Modeling and Analysis of HIV Evolution and Therapy | Nicolae Tarfulea (Purdue University, Calumet) |
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 | Coffee | Keller Hall 3-176 | W6.6-10.11 | |
| 9:00am-10:00am | Design of simultaneous source | Eldad Haber (University of British Columbia) | Keller Hall 3-180 | W6.6-10.11 |
| 10:00am-10:30am | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 10:30am-11:30am | Data Assimilation and Efficient Forward Modeling for Subsurface Flow | Louis 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 observations | David Higdon (Los Alamos National Laboratory) | Keller Hall 3-180 | W6.6-10.11 |
| 2:00pm-2:30pm | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 2:30pm-3:30pm | A map-based approach to Bayesian inference in inverse problems | Youssef Marzouk (Massachusetts Institute of Technology) | Keller Hall 3-180 | W6.6-10.11 |
| 3:30pm-3:45pm | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 3:45pm-4:15pm | NSF SEES Presentation | Rosemary Renaut (Arizona State University) | Keller Hall 3-180 | W6.6-10.11 |
| 4:15pm-7:00pm | Social event at Buffalo Wild Wings | Buffalo 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 | Coffee | Keller Hall 3-176 | W6.6-10.11 | |
| 9:00am-10:00am | Hierarchical Bayesian Models for Uncertainty Quantification and Model Validation | Roger G. Ghanem (University of Southern California) | Keller Hall 3-180 | W6.6-10.11 |
| 10:00am-10:30am | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 10:30am-11:30am | Discussion | Keller 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 techniques | Rosemary Renaut (Arizona State University) | Keller Hall 3-180 | W6.6-10.11 |
| 2:00pm-2:30pm | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 2:30pm-3:30pm | Bayesian Uncertainty Quantification for Subsurface Inversion using Multiscale Hierarchical Model | Bani K. Mallick (Texas A & M University) | Keller Hall 3-180 | W6.6-10.11 |
| 3:30pm-4:00pm | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 4:00pm-5:00pm | Climate Variability: Goals and Challenges | Juan 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 | Coffee | Keller Hall 3-176 | W6.6-10.11 | |
| 8:30am-9:30am | Hierarchical Bayesian Modeling: Why and How | Mark Berliner (Ohio State University) | Keller Hall 3-180 | W6.6-10.11 |
| 9:30am-9:45am | Coffee break | Keller 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 Applications | Christine A. Shoemaker (Cornell University) | Keller Hall 3-180 | W6.6-10.11 |
| 10:45am-11:00am | Coffee break | Keller Hall 3-176 | W6.6-10.11 | |
| 11:00am-12:00pm | Efficient estimates of prior information and uncertainty with chi-square tests | Jodi L. Mead () | Keller Hall 3-180 | W6.6-10.11 |
| 12:00pm-12:05pm | Closing remarks | Keller Hall 3-180 | W6.6-10.11 |
Monday, June 13
| 2:30pm-3:00pm | Coffee Break | Lind Hall 400 |
Wednesday, June 15
| 2:30pm-3:00pm | Coffee Break | Lind Hall 400 |
Thursday, June 16
| 2:30pm-3:00pm | Coffee Break | Lind Hall 400 |
Friday, June 17
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 |
Monday, June 20
| 9:00am-10:30am | Lecture 1 | Rafael 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 Applications | Peter 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 2 | Rafael de la Llave (University of Texas at Austin) | Lind Hall 305 | ND6.20-7.1.11 |
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 | ||
| 4:00pm-5:30pm | Lecture 1 | Àlex 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 Applications | Peter W. Bates (Michigan State University) | Lind Hall 305 | ND6.20-7.1.11 |
| 11:00am-12:30pm | Lecture 3 | Rafael 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 Applications | Peter W. Bates (Michigan State University) | Lind Hall 305 | ND6.20-7.1.11 |
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 | ||
| 4:00pm-5:30pm | TBA | George R Sell (University of Minnesota) | Lind Hall 305 | ND6.20-7.1.11 |
Wednesday, June 22
| 9:00am-10:30am | Lecture 4 | Rafael 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 Applications | Peter 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 break | Lind Hall 400 |
Thursday, June 23
| 9:00am-10:30am | Lecture 5 | Rafael 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 Applications | Peter 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 6 | Rafael de la Llave (University of Texas at Austin) | Lind Hall 305 | ND6.20-7.1.11 |
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 | ||
| 4:00pm-5:30pm | Huguet-Lecture 1 | Gemma 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 Applications | Peter W. Bates (Michigan State University) | Lind Hall 305 | ND6.20-7.1.11 |
| 11:00am-12:30pm | Lecture 7 | Rafael 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 Applications | Peter W. Bates (Michigan State University) | Lind Hall 305 | ND6.20-7.1.11 |
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 | ||
| 4:00pm-5:30pm | Lecture 2 | Àlex Haro Provinciale (University of Barcelona) | Lind Hall 305 | ND6.20-7.1.11 |
Monday, June 27
| 9:00am-10:30am | Lecture 8 | Rafael 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 Applications | Peter 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 2 | Gemma Huguet (Centre de Recerca Matemàtica ) | Lind Hall 305 | ND6.20-7.1.11 |
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 | ||
| 4:00pm-5:30pm | Lecture 3 | Àlex Haro Provinciale (University of Barcelona) | Lind Hall 305 | ND6.20-7.1.11 |
Tuesday, June 28
| 9:00am-10:30am | Lecture 9 | Rafael de la Llave (University of Texas at Austin) | Lind Hall 305 | ND6.20-7.1.11 |
| 11:00am-12:30pm | Exchange lemmas | Stephen 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 3 | Gemma Huguet (Centre de Recerca Matemàtica ) | Lind Hall 305 | ND6.20-7.1.11 |
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 | ||
| 4:00pm-5:30pm | Lecture 1 | Martin 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 Applications | Peter W. Bates (Michigan State University) | Lind Hall 305 | ND6.20-7.1.11 |
| 11:00am-12:30pm | Lecture 2 | Martin 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 break | Lind Hall 400 |
Thursday, June 30
| 9:00am-10:30am | Loss of normal hyperbolicity | Stephen Schecter (North Carolina State University) | Lind Hall 305 | ND6.20-7.1.11 |
| 11:00am-12:30pm | TBA | Zeng 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 speak | Lind Hall 305 | ND6.20-7.1.11 | |
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 | ||
| 4:00pm-5:30pm | Plus open problems | Lind Hall 305 | ND6.20-7.1.11 |
Friday, July 1
| 9:00am-10:30am | Other attendees speak | Lind Hall 305 | ND6.20-7.1.11 | |
| 11:00am-12:30pm | Plus open problems | Lind Hall 305 | ND6.20-7.1.11 | |
| 12:30pm-2:00pm | Lunch Break | ND6.20-7.1.11 | ||
| 2:30pm-3:00pm | Coffee break | Lind Hall 400 | ||
| 4:00pm-5:30pm | depart | Lind 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. |
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| 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. |
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| 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. |
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| 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) |
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| 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 |
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| 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. |
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| 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. |
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| 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 |
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| 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