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Compressive sensing adaptation for polynomial chaos expansions
v1v2 (latest)

Compressive sensing adaptation for polynomial chaos expansions

6 January 2018
Panagiotis Tsilifis
Xun Huan
Cosmin Safta
K. Sargsyan
G. Lacaze
J. Oefelein
H. Najm
R. Ghanem
ArXiv (abs)PDFHTML

Papers citing "Compressive sensing adaptation for polynomial chaos expansions"

12 / 12 papers shown
Title
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
136
5
0
30 Jan 2024
Uncertainty Quantification in Machine Learning for Engineering Design
  and Health Prognostics: A Tutorial
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
V. Nemani
Luca Biggio
Xun Huan
Zhen Hu
Olga Fink
Anh Tran
Yan Wang
Xiaoge Zhang
Chao Hu
AI4CE
154
96
0
07 May 2023
Sparse Bayesian Learning for Complex-Valued Rational Approximations
Sparse Bayesian Learning for Complex-Valued Rational Approximations
Felix Schneider
I. Papaioannou
Gerhard Muller
78
4
0
06 Jun 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
150
44
0
09 Mar 2022
A survey of unsupervised learning methods for high-dimensional
  uncertainty quantification in black-box-type problems
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
Katiana Kontolati
Dimitrios Loukrezis
D. D. Giovanis
Lohit Vandanapu
Michael D. Shields
146
47
0
09 Feb 2022
Sequential active learning of low-dimensional model representations for
  reliability analysis
Sequential active learning of low-dimensional model representations for reliability analysis
Max Ehre
I. Papaioannou
Bruno Sudret
D. Štraub
75
13
0
08 Jun 2021
A general framework of rotational sparse approximation in uncertainty
  quantification
A general framework of rotational sparse approximation in uncertainty quantification
Mengqi Hu
Y. Lou
Xiu Yang
69
0
0
13 Jan 2021
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian
  Processes
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
Panagiotis Tsilifis
Piyush Pandita
Sayan Ghosh
Valeria Andreoli
T. Vandeputte
Liping Wang
75
19
0
05 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
Sparse Polynomial Chaos expansions using Variational Relevance Vector
  Machines
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
Panagiotis Tsilifis
I. Papaioannou
D. Štraub
F. Nobile
156
20
0
23 Dec 2019
Extending classical surrogate modelling to high-dimensions through
  supervised dimensionality reduction: a data-driven approach
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach
C. Lataniotis
S. Marelli
Bruno Sudret
102
69
0
15 Dec 2018
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