Location
This workshop was held at Gross Hall on the campus of Duke University.
Description
The SAMSI program on Model Uncertainty: Mathematical and Statistical brings statisticians and applied mathematicians together with disciplinary scientists from a variety of fields, to better understand the effects of modeling and uncertainty on predictions. This workshop provides the foundation of the MUMS year, examining the theoretical basis for statistical uncertainty, the strengths and weaknesses of models of real world processes and the uncertainty in those processes, computational methods to solve model equations, and the degree of confidence in predictions and inferences resulting from the analysis.
Much of scientific activity of this program will arise from Working Groups – groups of scientists interested in a common theme, who will meet regularly during the year. On Thursday afternoon, a series of brief presentations will be scheduled, to seed the formation of Working Groups. Organizational meetings of these proposed groups will follow, and continue into Friday morning. Among the potential topics of Working Groups are: Foundations of Statistical Model Uncertainty; Modeling Across Scales; Materials Informatics and Mechanics; Reduced Order Models; Uncertainty in Extrapolative Settings; Biomedical Data and Precision Medicine (joint with the PMED program); Stochastic Discretization; Uncertainty in Geoscience; the Small Data problem; Uncertainty and Machine Learning. Multiple Working Groups on similar topics may be formed.
** Planning for this workshop is ongoing. As more information becomes available, it will be updated here **
Tentative Schedule and Supporting Media
Printable Schedule
Speaker Abstracts
Poster Session Titles
Participant List
Confirmed Speakers currently include:
- Amy Braverman (JPL/NASA)
- Jenny Brynjarsdóttir (Case Western Reserve)
- Kevin Carlberg (Sandia National Laboratories)
- Peter Challenor (University of Exeter)
- Merlise Clyde (Duke University)
- Michael Demkowicz (Texas A&M University)
- Michael Frenklach (University of California, Berkeley)
- Edward George (Wharton, University of Pennsylvania)
- Roger Ghanem (University of Southern California)
- Mengyang Gu (Johns Hopkins University)
- David Higdon (Virginia Tech University)
- Surya Kalidindi (Georgia Institute of Technology)
- Nathan Kutz (University of Washington)
- Bani Mallick (Texas A&M University)
- Robert Moser (University of Texas)
- Akil Narayan (University of Utah, Scientific Computing and Imaging (SCI) Institute)
- Tinsley Oden (University of Texas)
- Matthew Plumlee (Northwestern University)
- Krishna Rajan (University at Buffalo)
- Bruno Sansó (University of California, Santa Cruz)
- Leonard Smith (London School of Economics)
- Elaine Spiller (Marquette University)
- Laura Swiler (Sandia National Laboratories)
- Robert Wolpert (Duke University)
Monday, August 20, 2018
Gross Hall, Duke University, Durham, NC
Description | Speaker | Slides | Videos |
---|---|---|---|
Registration | |||
Welcome and Introductory Information | |||
Overview Lectures: | |||
Model Uncertainty and Uncertainty Quantification | Merlise Clyde, Duke University | ||
Principles of Predictive Computational Science: Predictive Models of Random Heterogeneous Materials and Tumor Growth | Tinsley Oden, University of Texas | ||
An Overview of Reduced-Order Models and Emulators | Elaine Spiller, Marquette University | ||
Theoretical Foundations of Model Uncertainty: | |||
Hierarchical Bayesian Models for Inverse Problems and Uncertainty Quantification | Bani Mallick, Texas A&M University | ||
On the Impact(s) of Structural Model Error on Simulation Modelling | Leonard Smith, London School of Economics, Pembroke College, Oxford | ||
Quantifying Nonparametric Modeling Uncertainty with BART | Edward George, Wharton, University of Pennsylvania | ||
Poster Session and Reception |
Tuesday, August 21, 2018
Gross Hall, Duke University, Durham, NC
Description | Speaker | Slides | Videos |
---|---|---|---|
The Isaac Newton Institute Uncertainty Quantification Programme: A Personal Perspective | Peter Challenor, University of Exetor | ||
Panel on Calibration in the Face of Model Discrepancy | Matthew Plumlee, Northwestern University | ||
Mengyang Gu, Johns Hopkins | |||
Georgios Karagiannis, University of Durham | |||
Model Reduction: | |||
Machine-Learning Error Models for Quantifying the Epistemic Uncertainty in Low-Fidelity Models | Kevin Carlberg, Sandia National Laboratories | ||
Emulators for models and Complexity Reduction | Akil Narayan, University of Utah | ||
Data-Driven Discovery of Governing Physical Laws and their Parametric Dependencies in Engineering, Physics and Biology | Nathan Kutz, University of Washington |
Wednesday, August 22, 2018
Gross Hall, Duke University, Durham, NC
Description | Speaker | Slides | Videos |
---|---|---|---|
Extrapolation: | |||
Extrapolation: The Art of Connecting Model-Based Predictions to Reality | David Higdon, Virginia Tech | ||
Bound-to-Bound-Data-Collaboration: Prediction on the Feasible Set | Michael Frenklach, University of California, Berkeley | ||
Model Discrepancy and Physical Parameters in Calibration and Prediction of Computer Models | Jenny Brynjarsdóttir, Case Western Reserve University | ||
Materials: | |||
Modeling and Algorithmic Aspects of UQ for Material with Multiscale Behavior | Roger Ghanem, University of Southern California | ||
Materials Innovation Driven by Data and Knowledge Systems | Surya Kalidindi, Georgia Institute of Technology | ||
Panel on Materials | Laura Swiler, Sandia National Laboratories | ||
Michael Demkowicz, Texas A&M University | |||
Ralph Smith, NC State University | |||
MUMS Workshop Social Event | Event sponsored by NC Chapter of ASA |
Thursday, August 23, 2018
Gross Hall, Duke University, Durham, NC
Description | Speaker | Slides | Videos |
---|---|---|---|
Model and Data Fusion: | |||
UQ Data Fusion: An Introduction and Case Study | Robert Wolpert, Duke University | ||
Model Uncertainty in Data Fusion for Remote Sensing | Amy Braverman, JPL/Caltech | ||
Inferring Release Characteristics from an Atmospheric Dispersion Model using Bayesian Adaptive Splines | Bruno Sanso, University of California, Santa Cruz | ||
Working Groups Overview/Proposals | |||
Working Groups Activity | Rooms: 304B, 318, 324, 359, 107 |
Friday, August 24, 2018
Gross Hall, Duke University, Durham, NC
Description | Speaker | Slides | Videos |
---|---|---|---|
Working Group Activity | Rooms: 304B, 318, 324, 359, 107 | ||
Working Groups Finalized | |||
Shuttle to RDU Airport |
Questions: email [email protected]