Probabilistic learning for modeling and quantifying model-form uncertainties in nonlinear computational mechanics

DOI10.1002/NME.5980zbMATH Open1548.7497MaRDI QIDQ6555406

Charbel Farhat, Christian Soize

Publication date: 14 June 2024

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

zbMATH Keywords

model reductionprobabilistic learningmachine learninguncertainty quantificationnonparametric probabilistic methodmodel-form uncertainties

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Nonparametric estimation (62G05) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)

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• Introduction to Stochastic Search and Optimization
• An Interior Point Algorithm for Large-Scale Nonlinear Programming
• Multivariate Density Estimation
• Probabilistic nonconvex constrained optimization with fixed number of function evaluations
• A nonparametric probabilistic approach for quantifying uncertainties in low-dimensional and high-dimensional nonlinear models

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