Gaussian process regression constrained by boundary value problems
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Publication:2060077
DOI10.1016/J.CMA.2021.114117OpenAlexW3208865524WikidataQ115063442 ScholiaQ115063442MaRDI QIDQ2060077
Publication date: 13 December 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2012.11857
boundary value problemboundary conditionscientific machine learningphysics-informedconstrained Gaussian process
Bayesian inference (62F15) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21)
Related Items (5)
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations ⋮ Covariance models and Gaussian process regression for the wave equation. Application to related inverse problems ⋮ Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis ⋮ A kernel framework for learning differential equations and their solution operators ⋮ On boundary conditions parametrized by analytic functions
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