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

The data-driven discovery of partial differential equations (PDEs) consistent with spatiotemporal data is experiencing a rebirth in machine learning research. Training deep neural networks to learn such data-driven partial differential operators requires extensive spatiotemporal data. For learning c...

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Veröffentlicht in:	JOM (1989) Jg. 72; H. 12; S. 4444 - 4457
Hauptverfasser:	Arbabi, Hassan, Bunder, Judith E., Samaey, Giovanni, Roberts, Anthony J., Kevrekidis, Ioannis G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2020 Springer Nature B.V
Schlagworte:	Applied mathematics Artificial neural networks Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification Chemistry/Food Science Data collection Earth Sciences Engineering Environment Homogenization Machine learning Material properties Mathematical models Neural networks Operators (mathematics) Partial differential equations Physics Scale models Simulation Spacetime Training
ISSN:	1047-4838, 1543-1851
Online-Zugang:	Volltext
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