Multi-stage Neural Networks: Function Approximator of Machine Precision

18 July 2023

Papers citing "Multi-stage Neural Networks: Function Approximator of Machine Precision"

13 / 13 papers shown

Title
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
Neural equilibria for long-term prediction of nonlinear conservation laws Jose Antonio Lara Benitez Junyi Guo Kareem Hegazy Ivan Dokmanić Michael W. Mahoney Maarten V. de Hoop 33 0 0 12 Jan 2025
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers Chuqi Chen Qixuan Zhou Yahong Yang Yang Xiang Tao Luo 29 1 0 08 Oct 2024
Spectrum-Informed Multistage Neural Networks: Multiscale Function Approximators of Machine Precision Jakin Ng Yongjian Wang Ching-Yao Lai 38 1 0 24 Jul 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 36 26 0 28 Jun 2024
On instabilities in neural network-based physics simulators Daniel Floryan AI4CE 33 2 0 18 Jun 2024
Physics informed cell representations for variational formulation of multiscale problems Yuxiang Gao Soheil Kolouri R. Duddu AI4CE 27 0 0 27 May 2024
Modelling Sampling Distributions of Test Statistics with Autograd A. A. Kadhim Harrison B. Prosper 22 0 0 03 May 2024
KAN: Kolmogorov-Arnold Networks Ziming Liu Yixuan Wang Sachin Vaidya Fabian Ruehle James Halverson Marin Soljacic Thomas Y. Hou Max Tegmark 78 473 0 30 Apr 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 24 3 0 15 Jan 2024
A multifidelity approach to continual learning for physical systems Amanda A. Howard Yucheng Fu P. Stinis AI4CE CLL 42 8 0 08 Apr 2023
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 40 209 0 16 Jul 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sifan Wang Hanwen Wang P. Perdikaris 131 438 0 18 Dec 2020