Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems

DOI10.1137/22m1513113zbMath1527.65003arXiv2204.02272OpenAlexW4386216217MaRDI QIDQ6062235

Eike H. Müller, Tony Shardlow, Unnamed Author

Publication date: 30 November 2023

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2204.02272

zbMATH Keywords

Bayesian inverse problemssurrogate modelsdeep learningdelayed-acceptance HMC

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Bayesian inference (62F15) Probabilistic models, generic numerical methods in probability and statistics (65C20) Applications to the sciences (65Z05) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32)

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• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
• Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
• An Introduction to Computational Stochastic PDEs
• An Introduction to Partial Differential Equations
• Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models

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