An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement-Reference-Cited by-同舟云学术

An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement

Published:2023-01-24 Issue:1 Volume:45 Page:B57-B77
ISSN:1064-8275
Container-title:SIAM Journal on Scientific Computing
language:en
Short-container-title:SIAM J. Sci. Comput.

Author:

Wu Keyi¹,Chen Peng²,Ghattas Omar³

Affiliation:

1. Department of Mathematics, The University of Texas at Austin, Austin, TX 78712 USA.

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

3. Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712 USA.

Funder

Multidisciplinary University Research Initiative

National Science Foundation

Simons Foundation

U.S. Department of Energy

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Computational Mathematics

Reference61 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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