Bayesian optimization with output-weighted optimal sampling

DOI10.1016/j.jcp.2020.109901OpenAlexW3045583938MaRDI QIDQ2123965

Antoine Blanchard, Themistoklis P. Sapsis

Publication date: 14 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.10599

zbMATH Keywords

optimal samplingextreme eventsBayesian optimization

Mathematics Subject Classification ID

Operations research, mathematical programming (90-XX) Information and communication theory, circuits (94-XX)

Related Items (5)

Scalable uncertainty quantification for deep operator networks using randomized priors ⋮ Output-weighted and relative entropy loss functions for deep learning precursors of extreme events ⋮ Multifidelity Bayesian Experimental Design to Quantify Rare-Event Statistics ⋮ Scalable Bayesian optimization with randomized prior networks ⋮ Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification

Uses Software

• EGO
• Spearmint
• Hyperopt
• Virtual library

Cites Work

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• Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples
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• Sequential sampling strategy for extreme event statistics in nonlinear dynamical systems

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