Sparse Compression of Expected Solution Operators
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Publication:5134977
DOI10.1137/20M132571XzbMath1475.65215arXiv1807.01741OpenAlexW3095387974MaRDI QIDQ5134977
Michael Feischl, Daniel Peterseim
Publication date: 18 November 2020
Published in: SIAM Journal on Numerical Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1807.01741
Probabilistic methods, particle methods, etc. for boundary value problems involving PDEs (65N75) Monte Carlo methods (65C05) PDEs with low regular coefficients and/or low regular data (35R05) Second-order elliptic equations (35J15) PDEs with randomness, stochastic partial differential equations (35R60) Numerical solutions to stochastic differential and integral equations (65C30) Numerical methods for partial differential equations, boundary value problems (65N99)
Related Items (9)
Numerical homogenization beyond scale separation ⋮ An offline-online strategy for multiscale problems with random defects ⋮ Multi-Resolution Localized Orthogonal Decomposition for Helmholtz Problems ⋮ Sparse Recovery of Elliptic Solvers from Matrix-Vector Products ⋮ A reduced basis super-localized orthogonal decomposition for reaction-convection-diffusion problems ⋮ Kernel methods are competitive for operator learning ⋮ Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity ⋮ A Priori Error Analysis of a Numerical Stochastic Homogenization Method ⋮ Super-localization of elliptic multiscale problems
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