Neural Network Approximation of Coarse-Scale Surrogates in Numerical Homogenization-Reference-Cited by-同舟云学术

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Neural Network Approximation of Coarse-Scale Surrogates in Numerical Homogenization

Published:2023-10-20 Issue:4 Volume:21 Page:1457-1485
ISSN:1540-3459
Container-title:Multiscale Modeling & Simulation
language:en
Short-container-title:Multiscale Model. Simul.

Author:

Kröpfl Fabian¹,Maier Roland²^ORCID,Peterseim Daniel³

Affiliation:

1. Institute of Mathematics, University of Augsburg, 86159, Augsburg, Germany.

2. Institute for Applied and Numerical Mathematics, Karlsruhe Institute of Technology, 76131, Karlsruhe, Germany.

3. Institute of Mathematics & Centre for Advanced Analytics and Predictive Sciences (CAAPS), University of Augsburg, 86159, Augsburg, Germany.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Computer Science Applications,General Physics and Astronomy,Ecological Modeling,Modeling and Simulation,General Chemistry

Reference40 articles.

1. Linking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations

2. Localized orthogonal decomposition method for the wave equation with a continuum of scales

3. Numerical homogenization beyond scale separation

4. Optimal Local Approximation Spaces for Generalized Finite Element Methods with Application to Multiscale Problems

5. A machine learning approach for efficient uncertainty quantification using multiscale methods

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