Automatic selection of basis-adaptive sparse polynomial chaos expansions
for engineering applications

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Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications

10 September 2020

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Papers citing "Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications"

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Title
Bayesian full waveform inversion with sequential surrogate model refinement G. Meles S. Marelli N. Linde 86 1 0 06 May 2025
UQ state-dependent framework for seismic fragility assessment of industrial components C. Nardin S. Marelli O. Bursi B. Sudret M. Broccardo 45 2 0 07 May 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
Reliability analysis for data-driven noisy models using active learning Anderson V. Pires M. Moustapha S. Marelli Bruno Sudret AI4CE 50 4 0 19 Jan 2024
A comprehensive framework for multi-fidelity surrogate modeling with noisy data: a gray-box perspective Katerina Giannoukou S. Marelli Bruno Sudret AI4CE 72 2 0 12 Jan 2024
Physics-Informed Polynomial Chaos Expansions Lukávs Novák Himanshu Sharma Michael D. Shields 92 22 0 04 Sep 2023
A spectral surrogate model for stochastic simulators computed from trajectory samples Nora Lüthen S. Marelli Bruno Sudret 63 22 0 12 Jul 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
Extreme learning machines for variance-based global sensitivity analysis John E. Darges A. Alexanderian P. Gremaud 110 2 0 14 Jan 2022
Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions Nora Lüthen O. Roustant Fabrice Gamboa Bertrand Iooss S. Marelli Bruno Sudret 164 5 0 01 Jul 2021