Adaptive Physics-informed Neural Networks: A Survey

23 March 2025

Papers citing "Adaptive Physics-informed Neural Networks: A Survey"

21 / 21 papers shown

Title
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators Zekun Shi Zheyuan Hu Min Lin Kenji Kawaguchi 408 8 0 27 Nov 2024
Parameterized Physics-informed Neural Networks for Parameterized PDEs Woojin Cho Minju Jo Haksoo Lim Kookjin Lee Dongeun Lee Sanghyun Hong Noseong Park PINN AI4CE 79 19 1 18 Aug 2024
Active Learning for Neural PDE Solvers Daniel Musekamp Marimuthu Kalimuthu David Holzmüller Makoto Takamoto Carlos Fernandez AI4CE 139 8 0 02 Aug 2024
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs Tomoharu Iwata Yusuke Tanaka N. Ueda AI4CE 73 2 0 20 Oct 2023
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs Yanlai Chen Shawn Koohy PINN AI4CE 71 28 0 27 Mar 2023
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows Raphael Pellegrin Blake Bullwinkel M. Mattheakis P. Protopapas PINN AI4CE 60 9 0 01 Nov 2022
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning M. Takamoto T. Praditia Raphael Leiteritz Dan MacKinlay Francesco Alesiani Dirk Pflüger Mathias Niepert AI4CE 76 237 0 13 Oct 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 91 383 0 21 Jul 2022
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations Keju Tang Xiaoliang Wan Chao Yang 47 115 0 28 Dec 2021
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems Jeremy Yu Lu Lu Xuhui Meng George Karniadakis PINN AI4CE 86 470 0 01 Nov 2021
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby PINN AI4CE 91 45 0 26 Oct 2021
One-Shot Transfer Learning of Physics-Informed Neural Networks Shaan Desai M. Mattheakis H. Joy P. Protopapas Stephen J. Roberts PINN AI4CE 73 58 0 21 Oct 2021
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 85 1,201 0 20 May 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 507 2,453 0 18 Oct 2020
Transfer learning based multi-fidelity physics informed deep neural network S. Chakraborty PINN OOD AI4CE 72 165 0 19 May 2020
Meta-Learning in Neural Networks: A Survey Timothy M. Hospedales Antreas Antoniou P. Micaelli Amos Storkey OOD 398 1,988 0 11 Apr 2020
A Comprehensive Survey on Transfer Learning Fuzhen Zhuang Zhiyuan Qi Keyu Duan Dongbo Xi Yongchun Zhu Hengshu Zhu Hui Xiong Qing He 188 4,474 0 07 Nov 2019
$Transfer learning enhanced physics informed neural network for phase-field modeling of fracture$ Transfer learning enhanced physics informed neural network for phase-field modeling of fracture S. Goswami C. Anitescu S. Chakraborty Timon Rabczuk PINN 81 612 0 04 Jul 2019
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 121 412 0 21 Sep 2018
On First-Order Meta-Learning Algorithms Alex Nichol Joshua Achiam John Schulman 240 2,238 0 08 Mar 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 833 11,952 0 09 Mar 2017