A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

21 July 2022

Papers citing "A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks"

42 / 42 papers shown

Title
Dual-Balancing for Physics-Informed Neural Networks Chenhong Zhou Jie Chen Zaifeng Yang Ching Eng Png PINN AI4CE 30 0 0 21 May 2025
Integration Matters for Learning PDEs with Backwards SDEs Sungje Park Stephen Tu PINN 55 0 0 02 May 2025
PIED: Physics-Informed Experimental Design for Inverse Problems Apivich Hemachandra Gregory Kang Ruey Lau Szu Hui Ng Bryan Kian Hsiang Low PINN 48 0 0 10 Mar 2025
Physics- and data-driven Active Learning of neural network representations for free energy functions of materials from statistical mechanics Jamie Holber Krishna Garikipati AI4CE 43 0 0 25 Feb 2025
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks A. Celaya Yimo Wang David T. Fuentes Beatrice Riviere 41 0 0 12 Feb 2025
The Finite Element Neural Network Method: One Dimensional Study Mohammed Abda Elsa Piollet Christopher Blake Frédérick P. Gosselin 71 0 0 21 Jan 2025
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks Youngsik Hwang Dong-Young Lim AI4CE 32 2 0 27 Sep 2024
Active Learning for Neural PDE Solvers Daniel Musekamp Marimuthu Kalimuthu David Holzmüller Makoto Takamoto Carlos Fernandez AI4CE 57 4 0 02 Aug 2024
Improved physics-informed neural network in mitigating gradient related failures Pancheng Niu Yongming Chen Jun Guo Yuqian Zhou Minfu Feng Yanchao Shi PINN AI4CE 26 0 0 28 Jul 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
Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN) Chenhao Si Ming Yan AI4CE PINN 41 3 0 05 Jun 2024
Astral: training physics-informed neural networks with error majorants V. Fanaskov Tianchi Yu Alexander Rudikov Ivan V. Oseledets 33 1 0 04 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
Accurate adaptive deep learning method for solving elliptic problems Jingyong Ying Yaqi Xie Jiao Li Hongqiao Wang 37 1 0 29 Apr 2024
Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs Jose Florido He-Nan Wang Amirul Khan P. Jimack 31 2 0 18 Apr 2024
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations Hongyan Li Song Jiang Wenjun Sun Liwei Xu Guanyu Zhou 27 2 0 04 Mar 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 26 17 0 05 Jan 2024
Adaptive importance sampling for Deep Ritz Xiaoliang Wan Tao Zhou Yuancheng Zhou 29 2 0 26 Oct 2023
Adversarial Training for Physics-Informed Neural Networks Yao Li Shengzhu Shi Zhichang Guo Boying Wu AAML PINN 27 0 0 18 Oct 2023
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation Antonio Ferrer-Sánchez Carlos Flores-Garrigós C. Hernani-Morales José J. Orquín-Marqués N. N. Hegade Alejandro Gomez Cadavid Iraitz Montalban Enrique Solano Yolanda Vives-Gilabert J. D. Martín-Guerrero 32 2 0 08 Sep 2023
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving Junjun Yan Xinhai Chen Zhichao Wang Enqiang Zhou Jie Liu PINN AI4CE 29 1 0 12 Jul 2023
Residual-based attention and connection to information bottleneck theory in PINNs Sokratis J. Anagnostopoulos Juan Diego Toscano Nikos Stergiopulos George Karniadakis 25 20 0 01 Jul 2023
Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm Shikhar Nilabh F. Grandia 39 1 0 14 Jun 2023
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems S. Mao R. Dong Lu Lu K. M. Yi Sizhuang He P. Perdikaris 14 16 0 18 May 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
Improving physics-informed neural networks with meta-learned optimization Alexander Bihlo PINN 36 18 0 13 Mar 2023
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal Francesco Di Fiore Michela Nardelli L. Mainini 37 22 0 02 Mar 2023
Achieving High Accuracy with PINNs via Energy Natural Gradients Johannes Müller Marius Zeinhofer 13 4 0 25 Feb 2023
Failure-informed adaptive sampling for PINNs, Part II: combining with re-sampling and subset simulation Zhi-Hao Gao Tao Tang Liang Yan Tao Zhou 37 18 0 03 Feb 2023
Physics-informed Neural Networks with Unknown Measurement Noise Philipp Pilar Niklas Wahlström PINN 23 6 0 28 Nov 2022
Physics-informed neural networks for gravity currents reconstruction from limited data Mickaël G. Delcey Y. Cheny S. Richter PINN AI4CE 19 11 0 03 Nov 2022
Failure-informed adaptive sampling for PINNs Zhiwei Gao Liang Yan Tao Zhou 18 77 0 01 Oct 2022
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method Tianhao Hu Bangti Jin Zhi Zhou 31 6 0 07 Sep 2022
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations Junho Choi N. Kim Youngjoon Hong AI4CE 24 0 0 21 Jul 2022
Improved Training of Physics-Informed Neural Networks with Model Ensembles Katsiaryna Haitsiukevich Alexander Ilin PINN 34 23 0 11 Apr 2022
Multi-Objective Loss Balancing for Physics-Informed Deep Learning Rafael Bischof M. Kraus PINN AI4CE 33 92 0 19 Oct 2021
Meta-learning PINN loss functions Apostolos F. Psaros Kenji Kawaguchi George Karniadakis PINN 43 97 0 12 Jul 2021
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 23 39 0 03 May 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 71 222 0 26 Apr 2021
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 101 274 0 20 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 39 494 0 09 Feb 2021