A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design-Reference-Cited by-同舟云学术

A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design

Published:2023-03-03 Issue:1 Volume:11 Page:235-261
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Wu Keyi¹,Chen Peng²,Ghattas Omar³

Affiliation:

1. Department of Mathematics, The University of Texas at Austin, Austin, TX 78705 USA.

2. School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30308 USA.

3. Departments of Geological Sciences and Mechanical Engineering and Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78705 USA.

Funder

U.S. Department of Energy

Multidisciplinary University Research Initiative

National Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference59 articles.

1. On Bayesian A- and D-Optimal Experimental Designs in Infinite Dimensions

2. A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized $\ell_0$-Sparsification

3. A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems

4. Mean-Variance Risk-Averse Optimal Control of Systems Governed by PDEs with Random Parameter Fields Using Quadratic Approximations

5. Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems

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