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Sparse Polynomial Chaos Expansions via Compressed Sensing and D-optimal
  Design

Sparse Polynomial Chaos Expansions via Compressed Sensing and D-optimal Design

29 December 2017
Paul Diaz
Alireza Doostan
Jerrad Hampton
ArXiv (abs)PDFHTML

Papers citing "Sparse Polynomial Chaos Expansions via Compressed Sensing and D-optimal Design"

13 / 13 papers shown
Title
On Fractional Moment Estimation from Polynomial Chaos Expansion
On Fractional Moment Estimation from Polynomial Chaos Expansion
Lukávs Novák
Marcos Valdebenito
Matthias Faes
81
6
0
04 Mar 2024
Polynomial Chaos Expansions on Principal Geodesic Grassmannian
  Submanifolds for Surrogate Modeling and Uncertainty Quantification
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification
Dimitris G. Giovanis
Dimitrios Loukrezis
Ioannis G. Kevrekidis
Michael D. Shields
128
5
0
30 Jan 2024
Active Learning-based Domain Adaptive Localized Polynomial Chaos
  Expansion
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion
Lukás Novák
Michael D. Shields
Václav Sadílek
M. Vořechovský
68
9
0
31 Jan 2023
Sensitivity-enhanced generalized polynomial chaos for efficient
  uncertainty quantification
Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification
Kyriakos D. Kantarakias
G. Papadakis
29
9
0
30 Jun 2022
Consistency regularization-based Deep Polynomial Chaos Neural Network
  Method for Reliability Analysis
Consistency regularization-based Deep Polynomial Chaos Neural Network Method for Reliability Analysis
Xiaohu Zheng
Wen Yao
Yunyang Zhang
Xiaoya Zhang
85
20
0
29 Mar 2022
On efficient algorithms for computing near-best polynomial
  approximations to high-dimensional, Hilbert-valued functions from limited
  samples
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
On the influence of over-parameterization in manifold based surrogates
  and deep neural operators
On the influence of over-parameterization in manifold based surrogates and deep neural operators
Katiana Kontolati
S. Goswami
Michael D. Shields
George Karniadakis
146
44
0
09 Mar 2022
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven
  Semi-supervised Method for Uncertainty Quantification
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification
Wen Yao
Xiaohu Zheng
Jun Zhang
Ning Wang
Guijian Tang
92
35
0
22 Jul 2021
Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty
  Quantification
Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty Quantification
F. Newberry
Jerrad Hampton
K. Jansen
Alireza Doostan
50
6
0
15 Apr 2021
Automatic selection of basis-adaptive sparse polynomial chaos expansions
  for engineering applications
Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications
Nora Lüthen
S. Marelli
Bruno Sudret
173
31
0
10 Sep 2020
Optimal Bayesian experimental design for subsurface flow problems
Optimal Bayesian experimental design for subsurface flow problems
Alexander Tarakanov
A. Elsheikh
73
10
0
10 Aug 2020
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
Nora Lüthen
S. Marelli
Bruno Sudret
169
171
0
04 Feb 2020
Variance-based sensitivity analysis for time-dependent processes
Variance-based sensitivity analysis for time-dependent processes
A. Alexanderian
P. Gremaud
Ralph C. Smith
121
54
0
21 Nov 2017
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