Learning Specialized Activation Functions for Physics-informed Neural Networks

8 August 2023

Gao Huang

Papers citing "Learning Specialized Activation Functions for Physics-informed Neural Networks"

22 / 22 papers shown

Title
STAF: Sinusoidal Trainable Activation Functions for Implicit Neural Representation Alireza Morsali MohammadJavad Vaez Mohammadhossein Soltani Amirhossein Kazerouni Babak Taati Morteza Mohammad-Noori 341 1 0 02 Feb 2025
Respecting causality is all you need for training physics-informed neural networks Sizhuang He Shyam Sankaran P. Perdikaris PINN CML AI4CE 131 200 0 14 Mar 2022
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks Jian Cheng Wong C. Ooi Abhishek Gupta Yew-Soon Ong AI4CE PINN SSL 56 79 0 20 Sep 2021
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions Ameya Dilip Jagtap Yeonjong Shin Kenji Kawaguchi George Karniadakis ODL 69 134 0 20 May 2021
Learning specialized activation functions with the Piecewise Linear Unit Yucong Zhou Zezhou Zhu Zhaobai Zhong 47 15 0 08 Apr 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 89 515 0 09 Feb 2021
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications N. Zobeiry K. D. Humfeld AI4CE 50 274 0 28 Sep 2020
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism L. McClenny U. Braga-Neto PINN 72 458 0 07 Sep 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 123 908 0 28 Jul 2020
Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks Colby Wight Jia Zhao 72 224 0 09 Jul 2020
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains Matthew Tancik Pratul P. Srinivasan B. Mildenhall Sara Fridovich-Keil N. Raghavan Utkarsh Singhal R. Ramamoorthi Jonathan T. Barron Ren Ng 116 2,414 0 18 Jun 2020
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks Alejandro Molina P. Schramowski Kristian Kersting ODL 34 80 0 15 Jul 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 95 1,525 0 10 Jul 2019
Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks Zhi-Qin John Xu Yaoyu Zhang Yaoyu Zhang Yan Xiao Zheng Ma 121 513 0 19 Jan 2019
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 102 408 0 21 Sep 2018
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 117 1,384 0 30 Sep 2017
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning Stefan Elfwing E. Uchibe Kenji Doya 130 1,719 0 10 Feb 2017
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 760 36,781 0 25 Aug 2016
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Djork-Arné Clevert Thomas Unterthiner Sepp Hochreiter 295 5,521 0 23 Nov 2015
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 154 2,800 0 20 Feb 2015
Learning Activation Functions to Improve Deep Neural Networks Forest Agostinelli Matthew Hoffman Peter Sadowski Pierre Baldi ODL 215 475 0 21 Dec 2014
Maxout Networks Ian Goodfellow David Warde-Farley M. Berk Mirza Aaron Courville Yoshua Bengio OOD 236 2,178 0 18 Feb 2013