Variational Monte Carlo -- bridging concepts of machine learning and high-dimensional partial differential equations
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Publication:2305540
DOI10.1007/s10444-019-09723-8zbMath1435.68258arXiv1810.01348OpenAlexW3103239355MaRDI QIDQ2305540
Martin Eigel, Philipp Trunschke, Sebastian Wolf, Reinhold Schneider
Publication date: 11 March 2020
Published in: Advances in Computational Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1810.01348
partial differential equationsmachine learninguncertainty quantificationstatistical learningtree tensor networks
General nonlinear regression (62J02) Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) PDEs with randomness, stochastic partial differential equations (35R60) Algorithms for approximation of functions (65D15) Multilinear algebra, tensor calculus (15A69) Numerical solution of discretized equations for boundary value problems involving PDEs (65N22)
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Uses Software
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