Reproducing Kernel Hilbert Spaces for Parametric Partial Differential Equations

DOI10.1137/15M1026870zbMath1371.35345MaRDI QIDQ5269856

Christian Rieger, Michael Griebel

Publication date: 28 June 2017

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

zbMATH Keywords

reproducing kernelsmachine learningparametric partial differential equationspower series kernelsdeterministic error analysis

Mathematics Subject Classification ID

Inverse problems for PDEs (35R30) PDEs with randomness, stochastic partial differential equations (35R60) Rate of convergence, degree of approximation (41A25) Algorithms for approximation of functions (65D15) Series expansions (e.g., Taylor, Lidstone series, but not Fourier series) (41A58)

Related Items (5)

Multilevel weighted least squares polynomial approximation ⋮ Reproducing kernels and choices of associated feature spaces, in the form of \(L^2\)-spaces ⋮ The Random Feature Model for Input-Output Maps between Banach Spaces ⋮ A reproducing kernel method for nonlinear C-q-fractional IVPs ⋮ Fast approximation by periodic kernel-based lattice-point interpolation with application in uncertainty quantification

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