Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression

DOI10.1016/j.camwa.2023.08.016arXiv2302.08216OpenAlexW4386475250MaRDI QIDQ6048987

Mengwu Guo, Andrea Manzoni, Ludovica Cicci, Stefania Fresca, Paolo Zunino

Publication date: 13 October 2023

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2302.08216

zbMATH Keywords

parameter estimationsensitivity analysisreduced order modelinguncertainty quantificationGaussian process regressionnonlinear solid mechanics

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99)

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