A framework for strategic discovery of credible neural network surrogate models under uncertainty

DOI10.1016/j.cma.2024.117061MaRDI QIDQ6557831

Kathryn Farrell-Maupin, Danial Faghihi, Pratyush Kumar Singh

Publication date: 18 June 2024

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

zbMATH Keywords

model validationuncertainty quantificationmodel plausibilitysurrogate modelingBayesian neural networks

Mathematics Subject Classification ID

Bayesian inference (62F15) Fluid-solid interactions (including aero- and hydro-elasticity, porosity, etc.) (74F10) Combustion (80A25) Numerical and other methods in solid mechanics (74S99) Elastic materials (74Bxx) Numerical approximation of high-dimensional functions; sparse grids (65D40)

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