On the Role of Fixed Points of Dynamical Systems in Training
Physics-Informed Neural Networks

On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks

25 March 2022

Franz M. Rohrhofer

Bernhard C. Geiger

Papers citing "On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks"

16 / 16 papers shown

Title
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks Bruno Jacob Amanda A. Howard P. Stinis 45 6 0 09 Nov 2024
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems Amanda A. Howard Bruno Jacob Sarah H. Murphy Alexander Heinlein P. Stinis AI4CE 44 26 0 28 Jun 2024
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs Simone Brivio S. Fresca Andrea Manzoni AI4CE 38 6 0 14 May 2024
PINNACLE: PINN Adaptive ColLocation and Experimental points selection Gregory Kang Ruey Lau Apivich Hemachandra See-Kiong Ng K. H. Low 3DPC 37 18 0 11 Apr 2024
Challenges in Training PINNs: A Loss Landscape Perspective Pratik Rathore Weimu Lei Zachary Frangella Lu Lu Madeleine Udell AI4CE PINN ODL 44 41 0 02 Feb 2024
Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems Alexander Heinlein Amanda A. Howard Damien Beecroft P. Stinis AI4CE 26 3 0 15 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
Stacked networks improve physics-informed training: applications to neural networks and deep operator networks Amanda A. Howard Sarah H. Murphy Shady E. Ahmed P. Stinis AI4CE 58 18 0 11 Nov 2023
Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp Dibyakanti Kumar Anirbit Mukherjee 31 2 0 08 Oct 2023
Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding Pi-Yueh Chuang L. Barba PINN 37 10 0 31 May 2023
A multifidelity approach to continual learning for physical systems Amanda A. Howard Yucheng Fu P. Stinis AI4CE CLL 50 8 0 08 Apr 2023
On the Generalization of PINNs outside the training domain and the Hyperparameters influencing it Andrea Bonfanti Roberto Santana M. Ellero Babak Gholami AI4CE PINN 43 3 0 15 Feb 2023
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling Arka Daw Jie Bu Sizhuang He P. Perdikaris Anuj Karpatne AI4CE 21 46 0 05 Jul 2022
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
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 50 494 0 09 Feb 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sizhuang He Hanwen Wang P. Perdikaris 131 439 0 18 Dec 2020