Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems

DOI10.1007/s10483-023-2997-7zbMath1518.76046arXiv2302.10448OpenAlexW4382894442MaRDI QIDQ6132292

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Publication date: 14 July 2023

Published in: AMM. Applied Mathematics and Mechanics. (English Edition) (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2302.10448

zbMATH Keywords

uncertainty quantificationgenerative adversarial networkHamiltonian Monte Carlo method100-dimensional Darcy problem

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Flows in porous media; filtration; seepage (76S05) Basic methods in fluid mechanics (76M99)

Related Items (1)

Preface: machine-learning approaches for computational mechanics

Cites Work

• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
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• Bayesian physics informed neural networks for real-world nonlinear dynamical systems
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons
• Solving high-dimensional partial differential equations using deep learning

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