Active learning with multifidelity modeling for efficient rare event simulation

DOI10.1016/j.jcp.2022.111506OpenAlexW3173802620MaRDI QIDQ2168325

Andrew E. Slaughter, Michael D. Shields, Chandrakanth Bolisetti, Somayajulu L. N. Dhulipala, Benjamin W. Spencer, Promit Chakroborty, Vincent M. Laboure

Publication date: 31 August 2022

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

Full work available at URL: https://arxiv.org/abs/2106.13790

zbMATH Keywords

reliabilityMonte Carlovariance reductionactive learninguncertainty quantificationmultifidelity modeling

Mathematics Subject Classification ID

Parametric inference (62Fxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (1)

Efficient Bayesian inference with latent Hamiltonian neural networks in no-U-turn sampling

Uses Software

• MOOSE
• AK-MCS

Cites Work

• Accelerated subset simulation with neural networks for reliability analysis
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• Multifidelity Approximate Bayesian Computation
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• Advanced Lectures on Machine Learning
• Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets

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