Divide and Conquer: An Incremental Sparsity Promoting Compressive
Sampling Approach for Polynomial Chaos Expansions

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Divide and Conquer: An Incremental Sparsity Promoting Compressive Sampling Approach for Polynomial Chaos Expansions

21 June 2016

Negin Alemazkoor

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Papers citing "Divide and Conquer: An Incremental Sparsity Promoting Compressive Sampling Approach for Polynomial Chaos Expansions"

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Title
On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 132 11 0 25 Mar 2022
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations Weiheng Zhong Hadi Meidani DRL 87 42 0 21 Mar 2022
A general framework of rotational sparse approximation in uncertainty quantification Mengqi Hu Y. Lou Xiu Yang 69 0 0 13 Jan 2021
Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications Nora Lüthen S. Marelli Bruno Sudret 165 31 0 10 Sep 2020
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark Nora Lüthen S. Marelli Bruno Sudret 169 171 0 04 Feb 2020
A General Framework for Enhancing Sparsity of Generalized Polynomial Chaos Expansions Xiu Yang Xiaoliang Wan Lin Lin H. Lei 68 10 0 10 Jul 2017
A Near-Optimal Sampling Strategy for Sparse Recovery of Polynomial Chaos Expansions Negin Alemazkoor Hadi Meidani 50 20 0 25 Feb 2017
Basis Adaptive Sample Efficient Polynomial Chaos (BASE-PC) Jerrad Hampton Alireza Doostan 100 44 0 03 Feb 2017