A Novel Variable-Separation Method Based on Sparse and Low Rank Representation for Stochastic Partial Differential Equations

DOI10.1137/16M1100010zbMath1379.65004arXiv1611.04093MaRDI QIDQ4597616

Qiuqi Li, Lijian Jiang

Publication date: 13 December 2017

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

Full work available at URL: https://arxiv.org/abs/1611.04093

zbMATH Keywords

sparse regularizationhierarchical sparse low rank tensor approximationimproved least angle regression algorithmnovel variable-separationnumerical exanples

Mathematics Subject Classification ID

Stochastic partial differential equations (aspects of stochastic analysis) (60H15) PDEs with randomness, stochastic partial differential equations (35R60) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (6)

A two-stage variable-separation Kalman filter for data assimilation ⋮ Stochastic domain decomposition based on variable-separation method ⋮ A Low-Rank Approximated Multiscale Method for Pdes With Random Coefficients ⋮ A hybrid model reduction method for stochastic parabolic optimal control problems ⋮ A Variable-Separation Method for Nonlinear Partial Differential Equations With Random Inputs ⋮ Multiscale model reduction for stochastic elasticity problems using ensemble variable-separated method

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