Location:
This workshop took place at SAMSI in Research Triangle Park, NC.
Description:
This workshop is intended to bring together active participants of Working Group I, The Statistical Inverse Problems group, which is run as a part of the 2016-2017 Program on Optimization.The areas of particular interest include sampling techniques for parameter estimation for large-scale Bayesian inverse problems, quantification of uncertainty, and optimal design of experiments for Bayesian inverse problems governed by Partial Differential Equations (PDE). Target applications include a wide class of problems ranging from the geosciences to medical imaging.
There are two main themes of this workshop:
- Optimal experimental design, which seeks to control experimental parameters to maximize the information gain about the estimated parameters of interest, subject to budget or physical constraints.
- Novel sampling techniques and the use of reduced order models to effectively sample high-dimensional distributions.
Schedule and Supporting Media
Thursday, January 26, 2017
SAMSI
| Description | Speaker | Slides | Videos |
|---|---|---|---|
| Registration | |||
| Opening Remarks | Ilse Ipsen, SAMSI Assoc. Director | ||
| Plenary:Computational Methodologies for Large Data Assimilation Problems | Adrian Sandu, Virginia Tech | ||
| Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems | Mathias Chung, Virginia Tech | ||
| Goal-Oriented Optimal Experimental Design | Ahmed Attia, SAMSI Post Doc | ||
| Optimal Experimental Design for Constrained Inverse Problems | Lars Ruthotto, Emory University | ||
| Gaussian Scale Mixtures for Inverse Problems in Imaging | Dirk Lorenz, Technische Universität Braunschweig |
Friday, January 27, 2017
SAMSI
| Description | Speaker | Slides | Videos |
|---|---|---|---|
| Plenary:Markov Chain Monte Carlo Algorithms for Linear Inverse Problems | John Bardsley, U of Montana | ||
| Hybrid Iterative Methods for Large-Scale Bayesian Inverse Problems | Julianne Chung, Virginia Tech | ||
| Computationally Efficient Markov Chain Monte Carlo Methods for Hierarchical Bayesian Inverse Problems | Andrew Brown, Clemson | ||
| Mitigating the Influence of the Boundary on PDE-based Covariance Operators | Georg Stadler, New York University | ||
| FINAL REMARKS | Alen Alexanderian and Arvind Saibaba, N.C. State University |
Questions: email [email protected]