Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
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20m1347486.pdf
Description
Published version
Size
2.39 MB
Format
Adobe PDF
Checksum (MD5)
e50b99174005bf54606294da0d921ab8
Author(s)
Blanchard, Antoine
Sapsis, Themistoklis
Date Issued
2021
Journal
SIAM/ASA Journal on Uncertainty Quantification
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Citation
Blanchard, Antoine and Sapsis, Themistoklis. 2021. "Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification." SIAM/ASA Journal on Uncertainty Quantification, 9 (2).
Version
Final published version
Abstract
We introduce a class of acquisition functions for sample selection that lead to faster convergence in applications related to Bayesian experimental design and uncertainty quantification. The approach follows the paradigm of active learning, whereby existing samples of a black-box function are utilized to optimize the next most informative sample. The proposed method aims to take advantage of the fact that some input directions of the black-box function have a larger impact on the output than others, which is important especially for systems exhibiting rare and extreme events. The acquisition functions introduced in this work leverage the properties of the likelihood ratio, a quantity that acts as a probabilistic sampling weight and guides the active-learning algorithm toward regions of the input space that are deemed most relevant. We demonstrate the proposed approach in the uncertainty quantification of a hydrological system as well as the probabilistic quantification of rare events in dynamical systems and the identification of their precursors in up to 30 dimensions.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1137/20M1347486
