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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of computational physics Ročník 404; číslo C; s. 109136
Hlavní autoři:	Jagtap, Ameya D., Kawaguchi, Kenji, Karniadakis, George Em
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cambridge Elsevier Inc 01.03.2020 Elsevier Science Ltd Elsevier
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!