Unveiling the optimization process of Physics Informed Neural Networks:
How accurate and competitive can PINNs be?

Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?

7 May 2024

Jorge F. Urbán

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Papers citing "Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?"

15 / 15 papers shown

Title
A practical PINN framework for multi-scale problems with multi-magnitude loss terms Yuanbo Wang Yanzhong Yao Jiawei Guo Zhiming Gao AI4CE 41 24 0 13 Aug 2023
Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies Alena Kopanicáková Hardik Kothari George Karniadakis Rolf Krause AI4CE 55 18 0 30 Jun 2023
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 86 377 0 21 Jul 2022
Physics-informed neural networks for inverse problems in supersonic flows Ameya Dilip Jagtap Zhiping Mao Nikolaus Adams George Karniadakis PINN 43 221 0 23 Feb 2022
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 79 1,198 0 20 May 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 243 236 0 23 Mar 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 97 520 0 09 Feb 2021
Physics Informed Neural Networks for Simulating Radiative Transfer Siddhartha Mishra Roberto Molinaro PINN 75 110 0 25 Sep 2020
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism L. McClenny U. Braga-Neto PINN 84 459 0 07 Sep 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 141 916 0 28 Jul 2020
A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks S. Dong Naxian Ni 79 137 0 15 Jul 2020
Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks Colby Wight Jia Zhao 77 225 0 09 Jul 2020
A Dual-Dimer Method for Training Physics-Constrained Neural Networks with Minimax Architecture Dehao Liu Yan Wang 129 75 0 01 May 2020
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks Georgios Kissas Yibo Yang E. Hwuang W. Witschey John A. Detre P. Perdikaris AI4CE 123 373 0 13 May 2019
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density Behrooz Ghorbani Shankar Krishnan Ying Xiao ODL 81 326 0 29 Jan 2019