Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models

DOI10.1016/j.jcp.2017.04.012zbMath1376.93020OpenAlexW2604390261MaRDI QIDQ1686574

Karen Willcox, Boris Kramer, Benjamin Peherstorfer

Publication date: 15 December 2017

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2017.04.012

zbMATH Keywords

model reductionuncertainty quantificationrare event simulationmultifidelitysurrogate modelingfailure probability estimation

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60)

Related Items

Ensemble Approximate Control Variate Estimators: Applications to MultiFidelity Importance Sampling, Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation, A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials, Rare event simulation for large-scale structures with local nonlinearities, Data-driven spatiotemporal modeling for structural dynamics on irregular domains by stochastic dependency neural estimation, Multifidelity approaches for uncertainty quantification, Multifidelity Dimension Reduction via Active Subspaces, Conditional-Value-at-Risk Estimation via Reduced-Order Models, Multifidelity probability estimation via fusion of estimators, Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters

Uses Software

• libagf

Cites Work

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