Physics Informed Extreme Learning Machine (PIELM) -- A rapid method for the numerical solution of partial differential equations

8 July 2019

Papers citing "Physics Informed Extreme Learning Machine (PIELM) -- A rapid method for the numerical solution of partial differential equations"

19 / 19 papers shown

Title
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines Hwijae Son 51 0 0 17 Jan 2025
Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems A. Thiruthummal Sergiy Shelyag Eun-Jin Kim 30 2 0 07 Jun 2024
Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations Michael D. Vander Wal R. McClarren K. Humbird AI4CE 22 4 0 28 May 2022
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data Xu Liu Wenjuan Yao Wei Peng Weien Zhou PINN AI4CE 49 25 0 14 May 2022
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks R. Mojgani Maciej Balajewicz P. Hassanzadeh PINN 33 45 0 05 May 2022
Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine Naxian Ni S. Dong 29 20 0 24 Apr 2022
Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach Evangelos Galaris Gianluca Fabiani I. Gallos Ioannis G. Kevrekidis Constantinos Siettos AI4CE 23 40 0 31 Jan 2022
Numerical Approximation of Partial Differential Equations by a Variable Projection Method with Artificial Neural Networks S. Dong Jielin Yang 40 17 0 24 Jan 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 26 1,180 0 14 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 62 384 0 06 Nov 2021
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems Jeremy Yu Lu Lu Xuhui Meng George Karniadakis PINN AI4CE 38 451 0 01 Nov 2021
PCNN: A physics-constrained neural network for multiphase flows Haoyang Zheng Ziyang Huang Guang Lin PINN 27 8 0 18 Sep 2021
Cell-average based neural network method for hyperbolic and parabolic partial differential equations Changxin Qiu Jue Yan 16 10 0 02 Jul 2021
Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks N. Sukumar Ankit Srivastava PINN AI4CE 41 241 0 17 Apr 2021
A Modified Batch Intrinsic Plasticity Method for Pre-training the Random Coefficients of Extreme Learning Machines S. Dong Zongwei Li 19 29 0 14 Mar 2021
SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs A. A. Ramabathiran P. Ramachandran PINN AI4CE 27 76 0 25 Feb 2021
Local Extreme Learning Machines and Domain Decomposition for Solving Linear and Nonlinear Partial Differential Equations S. Dong Zongwei Li 28 164 0 04 Dec 2020
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization Rishikesh Ranade C. Hill Jay Pathak AI4CE 54 123 0 17 May 2020
Distributed physics informed neural network for data-efficient solution to partial differential equations Vikas Dwivedi N. Parashar Balaji Srinivasan PINN 10 81 0 21 Jul 2019