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

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

20 May 2021

Ameya Dilip Jagtap

Kenji Kawaguchi

George Karniadakis

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

18 / 18 papers shown

Title
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs Sidharth S. Menon Ameya D. Jagtap PINN 178 0 0 06 May 2025
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 81 0 0 25 Apr 2025
STAF: Sinusoidal Trainable Activation Functions for Implicit Neural Representation Alireza Morsali MohammadJavad Vaez Hossein Soltani A. Kazerouni Babak Taati Morteza Mohammad-Noori 171 1 0 02 Feb 2025
A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure Yanjie Li Weijun Li Lina Yu Min Wu Jinyi Liu ... Xin Ning Yugui Zhang Baoli Lu Jian Xu Shuang Li 25 0 0 03 Jan 2024
Learning Specialized Activation Functions for Physics-informed Neural Networks Honghui Wang Lu Lu Shiji Song Gao Huang PINN AI4CE 16 12 0 08 Aug 2023
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks Ye Li Songcan Chen Shengyi Huang PINN 20 3 0 03 Mar 2023
Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem A. Akrim C. Gogu R. Vingerhoeds M. Salaün AI4CE 35 23 0 20 Jan 2023
Reliable extrapolation of deep neural operators informed by physics or sparse observations Min Zhu Handi Zhang Anran Jiao George Karniadakis Lu Lu 50 91 0 13 Dec 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 29 18 0 27 Oct 2022
How important are activation functions in regression and classification? A survey, performance comparison, and future directions Ameya Dilip Jagtap George Karniadakis AI4CE 37 71 0 06 Sep 2022
Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs) J. Abbasi Paal Ostebo Andersen PINN AI4CE 25 14 0 29 May 2022
Respecting causality is all you need for training physics-informed neural networks Sizhuang He Shyam Sankaran P. Perdikaris PINN CML AI4CE 49 199 0 14 Mar 2022
Physics-informed neural networks for inverse problems in supersonic flows Ameya Dilip Jagtap Zhiping Mao Nikolaus Adams George Karniadakis PINN 26 201 0 23 Feb 2022
Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process Thi Nguyen Khoa Nguyen T. Dairay Raphael Meunier Mathilde Mougeot PINN AI4CE 81 29 0 31 Jan 2022
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 101 274 0 20 Apr 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sizhuang He Hanwen Wang P. Perdikaris 131 439 0 18 Dec 2020
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains Ziqi Liu Wei Cai Zhi-Qin John Xu AI4CE 264 122 0 22 Jul 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 128 509 0 11 Mar 2020