Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

•We employed adaptive activation functions in deep and physics-informed neural networks.•The proposed method is very simple and it is shown to accelerate convergence in neural networks.•In particular, we approximate various nonlinear functions using deep neural networks.•Physics-informed neural netw...

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Veröffentlicht in:	Journal of computational physics Jg. 404; H. C; S. 109136
Hauptverfasser:	Jagtap, Ameya D., Kawaguchi, Kenji, Karniadakis, George Em
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge Elsevier Inc 01.03.2020 Elsevier Science Ltd Elsevier
Schlagworte:	Activation Algorithms Bad minima Burgers equation Computational physics Computer simulation Convergence Critical point Deep learning benchmarks Frequencies Helmholtz equations Inverse problems Klein-Gordon equation Machine learning Mathematical analysis Neural networks Nonlinear differential equations Nonlinear equations Optimization Parameter identification Partial differential equations Physics-informed neural networks Topology Deep learning benchmarks Bad minima Inverse problems Partial differential equations Physics-informed neural networks Machine learning
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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