Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism

7 September 2020

Papers citing "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism"

43 / 43 papers shown

Title
Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method Conor Rowan K. Maute Alireza Doostan AI4CE 33 0 0 08 May 2025
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs Sidharth S. Menon Ameya D. Jagtap PINN 78 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 76 0 0 25 Apr 2025
Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble Zongren Zou Zhicheng Wang George Karniadakis PINN AI4CE 65 2 0 08 Mar 2025
Unraveling particle dark matter with Physics-Informed Neural Networks M.P. Bento H.B. Câmara J.F. Seabra 53 0 0 24 Feb 2025
The Finite Element Neural Network Method: One Dimensional Study Mohammed Abda Elsa Piollet Christopher Blake Frédérick P. Gosselin 56 0 0 21 Jan 2025
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning Nilo Schwencke Cyril Furtlehner 64 1 0 14 Dec 2024
ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification Siva Viknesh Younes Tatari Amirhossein Arzani 26 1 0 21 Oct 2024
Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics Vineet Jagadeesan Nair PINN 38 0 0 10 Oct 2024
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases Madison Cooley Varun Shankar Robert M. Kirby Shandian Zhe 22 2 0 04 Oct 2024
CMINNs: Compartment Model Informed Neural Networks -- Unlocking Drug Dynamics Nazanin Ahmadi Daryakenari Shupeng Wang George Karniadakis 63 3 0 19 Sep 2024
KAN: Kolmogorov-Arnold Networks Ziming Liu Yixuan Wang Sachin Vaidya Fabian Ruehle James Halverson Marin Soljacic Thomas Y. Hou Max Tegmark 72 460 0 30 Apr 2024
Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs Jose Florido He-Nan Wang Amirul Khan P. Jimack 29 2 0 18 Apr 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 18 17 0 05 Jan 2024
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations Taniya Kapoor Hongrui Wang Alfredo Núñez R. Dollevoet AI4CE PINN 6 15 0 01 Nov 2023
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 18 9 0 08 Oct 2023
Learning Specialized Activation Functions for Physics-informed Neural Networks Honghui Wang Lu Lu Shiji Song Gao Huang PINN AI4CE 8 11 0 08 Aug 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 24 1 0 12 Jul 2023
Learning Koopman Operators with Control Using Bi-level Optimization Daning Huang Abhinav Pandey Yin Yu Junyi Geng AI4CE 11 3 0 12 Jul 2023
Temporal Difference Learning for High-Dimensional PIDEs with Jumps Liwei Lu Hailong Guo Xueqing Yang Yi Zhu AI4CE 10 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 17 20 0 01 Jul 2023
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs Yanlai Chen Shawn Koohy PINN AI4CE 18 24 0 27 Mar 2023
PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss Fabian Heldmann Sarah Treibert Matthias Ehrhardt K. Klamroth 22 20 0 03 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 17 18 0 03 Feb 2023
Deep Learning and Computational Physics (Lecture Notes) Deep Ray Orazio Pinti Assad A. Oberai PINN AI4CE 19 7 0 03 Jan 2023
Physics-informed Neural Networks with Unknown Measurement Noise Philipp Pilar Niklas Wahlström PINN 18 6 0 28 Nov 2022
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing Salah A. Faroughi N. Pawar C. Fernandes Maziar Raissi Subasish Das N. Kalantari S. K. Mahjour PINN AI4CE 18 47 0 14 Nov 2022
A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency Weilong Guan Kai-Ping Yang Yinsheng Chen Zhong Guan PINN AI4CE 8 12 0 19 Oct 2022
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations Alexander New B. Eng A. Timm A. Gearhart 12 4 0 14 Oct 2022
Failure-informed adaptive sampling for PINNs Zhiwei Gao Liang Yan Tao Zhou 16 76 0 01 Oct 2022
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method Tianhao Hu Bangti Jin Zhi Zhou 21 6 0 07 Sep 2022
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chen-Chun Wu M. Zhu Qinyan Tan Yadhu Kartha Lu Lu 16 351 0 21 Jul 2022
Scalable algorithms for physics-informed neural and graph networks K. Shukla Mengjia Xu N. Trask George Karniadakis PINN AI4CE 59 39 0 16 May 2022
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems K. Linka Amelie Schäfer Xuhui Meng Zongren Zou George Karniadakis E. Kuhl OOD PINN AI4CE 22 109 0 12 May 2022
Improved Training of Physics-Informed Neural Networks with Model Ensembles Katsiaryna Haitsiukevich Alexander Ilin PINN 22 23 0 11 Apr 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 67 29 0 31 Jan 2022
Physics-Informed Neural Operator for Learning Partial Differential Equations Zong-Yi Li Hongkai Zheng Nikola B. Kovachki David Jin Haoxuan Chen Burigede Liu Kamyar Azizzadenesheli Anima Anandkumar AI4CE 30 377 0 06 Nov 2021
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks Xiang Huang Hongsheng Liu Beiji Shi Zidong Wang Kan Yang ... Jing Zhou Fan Yu Bei Hua Lei Chen Bin Dong 11 20 0 02 Nov 2021
Multi-Objective Loss Balancing for Physics-Informed Deep Learning Rafael Bischof M. Kraus PINN AI4CE 20 88 0 19 Oct 2021
Improved architectures and training algorithms for deep operator networks Sifan Wang Hanwen Wang P. Perdikaris AI4CE 42 103 0 04 Oct 2021
Long-time integration of parametric evolution equations with physics-informed DeepONets Sifan Wang P. Perdikaris AI4CE 17 117 0 09 Jun 2021
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 13 39 0 03 May 2021
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 273 2,878 0 15 Sep 2016