Enabling risk and reliability analysis in next-generation computational tools via multifidelity methods
Abstract
Risk and reliability analysis are fundamental to the design, assessment, and operation of safety-critical complex systems; however, their integration into next-generation computational tools is constrained by the cost of uncertainty quantification with expensive computational models. The computational challenge is particularly prohibitive for tail-sensitive quantities, such as probabilities of failure, conditional values-at-risk, and buffered probabilities of failure, where accurate estimation can require a large number of model evaluations. Multifidelity methods provide a framework for addressing this computational bottleneck by combining information from models of varying accuracy and cost while retaining the predictive accuracy of the high-fidelity model. This review summarizes the state of the art in methods that enable scalable risk and reliability analysis using multifidelity control variates, multifidelity importance sampling, and multifidelity active learning.
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BibTeX
@misc{chaudhuri2026enabling,
title = {{Enabling risk and reliability analysis in next-generation computational tools via multifidelity methods}},
author = {Anirban Chaudhuri and Karen Willcox},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.05087},
eprint = {2611.05087},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.05087},
}
RIS
TY - UNPB
TI - Enabling risk and reliability analysis in next-generation computational tools via multifidelity methods
AU - Anirban Chaudhuri
AU - Karen Willcox
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.05087
UR - https://arxiv.org/abs/2611.05087
AB - Risk and reliability analysis are fundamental to the design, assessment, and operation of safety-critical complex systems; however, their integration into next-generation computational tools is constrained by the cost of uncertainty quantification with expensive computational models. The computational challenge is particularly prohibitive for tail-sensitive quantities, such as probabilities of failure, conditional values-at-risk, and buffered probabilities of failure, where accurate estimation can require a large number of model evaluations. Multifidelity methods provide a framework for addressing this computational bottleneck by combining information from models of varying accuracy and cost while retaining the predictive accuracy of the high-fidelity model. This review summarizes the state of the art in methods that enable scalable risk and reliability analysis using multifidelity control variates, multifidelity importance sampling, and multifidelity active learning.
ER -