Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks

9 July 2020

Papers citing "Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks"

36 / 36 papers shown

Title
Learning and Transferring Physical Models through Derivatives Alessandro Trenta Andrea Cossu Davide Bacciu AI4CE 39 0 0 02 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
Solving 2-D Helmholtz equation in the rectangular, circular, and elliptical domains using neural networks D. Veerababu Prasanta K. Ghosh 73 0 0 26 Mar 2025
Machine learning for modelling unstructured grid data in computational physics: a review Sibo Cheng Marc Bocquet Weiping Ding Tobias S. Finn Rui Fu ... Yong Zeng Mingrui Zhang Hao Zhou Kewei Zhu Rossella Arcucci PINN AI4CE 114 0 0 13 Feb 2025
Learn Sharp Interface Solution by Homotopy Dynamics Chuqi Chen Yahong Yang Yang Xiang Wenrui Hao ODL 62 1 0 01 Feb 2025
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases Madison Cooley Varun Shankar Robert M. Kirby Shandian Zhe 32 2 0 04 Oct 2024
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study Milad Ramezankhani A. Deodhar Rishi Parekh Dagnachew Birru AI4CE 50 3 0 20 Jun 2024
Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks R. Mattey Susanta Ghosh AI4CE 43 1 0 09 May 2024
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be? Jorge F. Urbán P. Stefanou José A. Pons PINN 45 6 0 07 May 2024
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes Ming Zhong Dehao Liu Raymundo Arroyave U. Braga-Neto AI4CE SSL 26 1 0 08 Apr 2024
Binary structured physics-informed neural networks for solving equations with rapidly changing solutions Yanzhi Liu Ruifan Wu Ying Jiang PINN 26 2 0 23 Jan 2024
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective Sunwoong Yang Hojin Kim Y. Hong K. Yee R. Maulik Namwoo Kang PINN AI4CE 31 17 0 05 Jan 2024
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology Qingsong Xu Yilei Shi Jonathan Bamber Ye Tuo Ralf Ludwig Xiao Xiang Zhu AI4CE 20 10 0 08 Oct 2023
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
Temporal Difference Learning for High-Dimensional PIDEs with Jumps Liwei Lu Hailong Guo Xueqing Yang Yi Zhu AI4CE 23 6 0 06 Jul 2023
Residual-based attention and connection to information bottleneck theory in PINNs Sokratis J. Anagnostopoulos Juan Diego Toscano Nikos Stergiopulos George Karniadakis 27 20 0 01 Jul 2023
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs Yuling Jiao Dingwei Li Xiliang Lu J. Yang Cheng Yuan 34 9 0 28 Mar 2023
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs Yanlai Chen Shawn Koohy PINN AI4CE 37 24 0 27 Mar 2023
Neural tangent kernel analysis of PINN for advection-diffusion equation M. Saadat B. Gjorgiev L. Das G. Sansavini 35 0 0 21 Nov 2022
Physics-Informed Neural Networks for Shell Structures Jan-Hendrik Bastek D. Kochmann AI4CE 18 51 0 26 Jul 2022
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chen-Chun Wu Min Zhu Qinyan Tan Yadhu Kartha Lu Lu 32 353 0 21 Jul 2022
Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? Chuwei Wang Shanda Li Di He Liwei Wang AI4CE PINN 31 28 0 04 Jun 2022
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks R. Mojgani Maciej Balajewicz Pedram Hassanzadeh PINN 33 45 0 05 May 2022
Improved Training of Physics-Informed Neural Networks with Model Ensembles Katsiaryna Haitsiukevich Alexander Ilin PINN 39 23 0 11 Apr 2022
On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 41 17 0 25 Mar 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 solving parametric magnetostatic problems Andrés Beltrán-Pulido Ilias Bilionis D. Aliprantis 30 34 0 08 Feb 2022
Multi-Objective Loss Balancing for Physics-Informed Deep Learning Rafael Bischof M. Kraus PINN AI4CE 33 92 0 19 Oct 2021
Physics informed neural networks for continuum micromechanics Alexander Henkes Henning Wessels R. Mahnken PINN AI4CE 16 139 0 14 Oct 2021
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training E. Haghighat Daniel Amini R. Juanes PINN AI4CE 23 95 0 06 Oct 2021
Variational Physics Informed Neural Networks: the role of quadratures and test functions S. Berrone C. Canuto Moreno Pintore 31 41 0 05 Sep 2021
IDRLnet: A Physics-Informed Neural Network Library Wei Peng Jun Zhang Weien Zhou Xiaoyu Zhao W. Yao Xiaoqian Chen PINN AI4CE 35 15 0 09 Jul 2021
Long-time integration of parametric evolution equations with physics-informed DeepONets Sizhuang He P. Perdikaris AI4CE 24 117 0 09 Jun 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 30 146 0 22 Dec 2020
Discovery of Governing Equations with Recursive Deep Neural Networks Jia Zhao Jarrod Mau PINN 32 6 0 24 Sep 2020
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism L. McClenny U. Braga-Neto PINN 28 444 0 07 Sep 2020