Stein Variational Reduced Basis Bayesian Inversion

DOI10.1137/20M1321589zbMath1467.62042arXiv2002.10924OpenAlexW3135985701MaRDI QIDQ4997362

Omar Ghattas, Peng Chen

Publication date: 29 June 2021

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.10924

zbMATH Keywords

error analysismodel reductionuncertainty quantificationBayesian inverse problemsvariational inferencereduced basis

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Probabilistic models, generic numerical methods in probability and statistics (65C20) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32) Probabilistic methods, particle methods, etc. for initial value and initial-boundary value problems involving PDEs (65M75)

Related Items

Learning physics-based models from data: perspectives from inverse problems and model reduction, VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems, Stein Variational Gradient Descent on Infinite-Dimensional Space and Applications to Statistical Inverse Problems, A low-rank solver for parameter estimation and uncertainty quantification in time-dependent systems of partial differential equations, Bayesian inference of heterogeneous epidemic models: application to COVID-19 spread accounting for long-term care facilities

Uses Software

• redbKIT

Cites Work

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