Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations

DOI10.1137/23m1560574OpenAlexW4391353536WikidataQ129136843 ScholiaQ129136843MaRDI QIDQ6154967

Min-Seok Choi, Unnamed Author, Unnamed Author

Publication date: 16 February 2024

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

Full work available at URL: https://doi.org/10.1137/23m1560574

zbMATH Keywords

stochastic partial differential equationuncertainty quantificationHamiltonian Monte CarloBayesian neural network

Mathematics Subject Classification ID

PDEs with randomness, stochastic partial differential equations (35R60) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Neural nets and related approaches to inference from stochastic processes (62M45)

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