Nonlinear dimension reduction for surrogate modeling using gradient information

DOI10.1093/imaiai/iaac006zbMath1505.65155arXiv2102.10351OpenAlexW4226291820MaRDI QIDQ5878248

Clémentine Prieur, Olivier Zahm, Youssef M. Marzouk, Daniele Bigoni

Publication date: 20 February 2023

Published in: Information and Inference: A Journal of the IMA (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2102.10351

zbMATH Keywords

Poincaré inequalityhigh-dimensional approximationnonlinear dimension reductionfeature mapadaptive polynomial approximation

Mathematics Subject Classification ID

Multidimensional problems (41A63) Approximation by polynomials (41A10) Algorithms for approximation of functions (65D15) Numerical approximation of high-dimensional functions; sparse grids (65D40)

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Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems ⋮ Model Reduction for Nonlinear Systems by Balanced Truncation of State and Gradient Covariance ⋮ Conditional Karhunen-Loève regression model with basis adaptation for high-dimensional problems: uncertainty quantification and inverse modeling

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