Least Squares Polynomial Chaos Expansion: A Review of Sampling Strategies

23 June 2017

Papers citing "Least Squares Polynomial Chaos Expansion: A Review of Sampling Strategies"

17 / 17 papers shown

Title
Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis Geremy Loachamín Suntaxi Paris Papavasileiou E. D. Koronaki Dimitrios G. Giovanis G. Gakis ... M. Kathrein Gabriele Pozzetti Christoph Czettl Stéphane P. A. Bordas A. Boudouvis 63 0 0 24 May 2024
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 86 6 0 04 Apr 2024
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
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy Wyatt Bridgman Uma Balakrishnan Reese E. Jones Jiefu Chen Xuqing Wu Cosmin Safta Yueqin Huang Mohammad Khalil 55 0 0 07 Dec 2023
A Stable Jacobi polynomials based least squares regression estimator associated with an ANOVA decomposition model Mohamed Jebalia Abderrazek Karoui 31 0 0 04 Aug 2022
Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification Kyriakos D. Kantarakias G. Papadakis 17 9 0 30 Jun 2022
Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics J. Akian L. Bonnet H. Owhadi Éric Savin 125 28 0 03 Jun 2022
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms Paul-Christian Bürkner Ilja Kroker S. Oladyshkin Wolfgang Nowak 111 11 0 12 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 116 11 0 25 Mar 2022
On the influence of over-parameterization in manifold based surrogates and deep neural operators Katiana Kontolati S. Goswami Michael D. Shields George Karniadakis 134 44 0 09 Mar 2022
Nonintrusive Uncertainty Quantification for automotive crash problems with VPS/Pamcrash M. Rocas Alberto García-González S. Zlotnik X. Larráyoz Pedro Díez 42 10 0 15 Feb 2021
$Sparse Identification of Nonlinear Dynamical Systems via Reweighted $\ell_1$-regularized Least Squares$ Sparse Identification of Nonlinear Dynamical Systems via Reweighted $\ell_1$ -regularized Least Squares A. Cortiella K. Park Alireza Doostan 65 77 0 27 May 2020
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark Nora Lüthen S. Marelli Bruno Sudret 169 171 0 04 Feb 2020
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design Piyush Pandita Nimish Awalgaonkar Ilias Bilionis Jitesh H. Panchal 97 1 0 16 Dec 2019
Sequential sampling for optimal weighted least squares approximations in hierarchical spaces B. Arras M. Bachmayr A. Cohen 40 22 0 28 May 2018
Sparse Polynomial Chaos Expansions via Compressed Sensing and D-optimal Design Paul Diaz Alireza Doostan Jerrad Hampton 94 102 0 29 Dec 2017
Basis Adaptive Sample Efficient Polynomial Chaos (BASE-PC) Jerrad Hampton Alireza Doostan 88 44 0 03 Feb 2017