An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

Partial Differential Equations (PDEs) are fundamental to model different phenomena in science and engineering mathematically. Solving them is a crucial step towards a precise knowledge of the behavior of natural and engineered systems. In general, in order to solve PDEs that represent real systems t...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 362; S. 112790
Hauptverfasser:	Samaniego, E., Anitescu, C., Goswami, S., Nguyen-Thanh, V.M., Guo, H., Hamdia, K., Zhuang, X., Rabczuk, T.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.04.2020 Elsevier BV
Schlagworte:	Algorithms Approximation Artificial neural networks Computational mechanics Deep neural networks Energy approach Finite element method Machine learning Mathematical analysis Mechanical systems Meshless methods Methods Partial differential equations Physics informed Physics informed Energy approach Deep neural networks
ISSN:	0045-7825, 1879-2138
Online-Zugang:	Volltext
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