← 研踪 PaperTrailarXiv:2611.05087 · v1
Enabling risk and reliability analysis in next-generation computational tools via multifidelity methods
首次提交 2026-11-03 23:45
版本更新 2026-11-03 23:45
雷达观测 2026-11-04 23:39
摘要
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.
作者备注：Review article
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-03 23:45 · 最新观测版本
Enabling risk and reliability analysis in next-generation computational tools via multifidelity methods
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.
查看此版本解读 →查看此版本原文 ↗