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XPINNs
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential EquationsConservative_PINNs
We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.Locally-Adaptive-Activation-Functions-Neural-Networks-
Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawaguchi, G E Karniadakis, Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 20200334, 2020. (http://dx.doi.org/10.1098/rspa.2020.0334)".XPINNs_TensorFlow-2
XPINN code written in TensorFlow 2Adaptive_Activation_Functions
We proposed the simple adaptive activation functions deep neural networks. The proposed method is simple and easy to implement in any neural networks architecture.Error_estimates_PINN_and_XPINN_NonlinearPDEs
The first comprehensive theoretical analysis of PINNs (and XPINNs) for a prototypical nonlinear PDE, the Navier-Stokes equations are given.Physics_Informed_Deep_Learning
Short course on physics-informed deep learningAugmented_PINNs_-APINNs-
Activation-functions-in-regression-and-classification
How important are How important are activation functions in regression and classification? A survey, performance comparison, and future directionsLove Open Source and this site? Check out how you can help us