Papers citing 'Enhancing Sparsity of Hermite Polynomial Expansions by Iterative Rotations'

Title
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification Jiahao Zhang Shiqi Zhang Guang Lin 49 0 0 11 Apr 2022
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems Jiahao Zhang Shiqi Zhang Guang Lin 122 16 0 07 Apr 2022
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
A general framework of rotational sparse approximation in uncertainty quantification Mengqi Hu Y. Lou Xiu Yang 69 0 0 13 Jan 2021
Compressive sensing adaptation for polynomial chaos expansions Panagiotis Tsilifis Xun Huan Cosmin Safta K. Sargsyan G. Lacaze J. Oefelein H. Najm R. Ghanem 114 61 0 06 Jan 2018
Sparse Polynomial Chaos Expansions via Compressed Sensing and D-optimal Design Paul Diaz Alireza Doostan Jerrad Hampton 102 102 0 29 Dec 2017
A General Framework for Enhancing Sparsity of Generalized Polynomial Chaos Expansions Xiu Yang Xiaoliang Wan Lin Lin H. Lei 72 10 0 10 Jul 2017
A Near-Optimal Sampling Strategy for Sparse Recovery of Polynomial Chaos Expansions Negin Alemazkoor Hadi Meidani 58 20 0 25 Feb 2017
Gradient-informed basis adaptation for Legendre Chaos expansions Panagiotis Tsilifis 110 4 0 08 Nov 2016
Divide and Conquer: An Incremental Sparsity Promoting Compressive Sampling Approach for Polynomial Chaos Expansions Negin Alemazkoor Hadi Meidani 87 26 0 21 Jun 2016