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How important are activation functions in regression and classification? A survey, performance comparison, and future directions


Sep 13, 2022
Ameya D. Jagtap, George Em Karniadakis

* 47 pages, 14 figures 

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Error estimates for physics informed neural networks approximating the Navier-Stokes equations


Mar 17, 2022
Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra


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Physics-informed neural networks for inverse problems in supersonic flows


Feb 23, 2022
Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams, George Em Karniadakis

* 19 pages, 20 figures 

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When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?


Oct 12, 2021
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi

* 32 pages, 11 figures 

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Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions


May 20, 2021
Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis

* 26 pages, 13 figures 

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Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks


Oct 18, 2019
Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis

* 19 pages, 13 figures 

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