Utilising physics-guided deep learning to overcome data scarcity

24 November 2022

Papers citing "Utilising physics-guided deep learning to overcome data scarcity"

7 / 7 papers shown

Title
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEs Jinshuai Bai Guirong Liu Ashish Gupta Laith Alzubaidi Xinzhu Feng Yuantong T. Gu PINN 14 1 0 13 Apr 2023
Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps Carlos Ruiz Herrera Thomas Grandits Gernot Plank P. Perdikaris F. Sahli Costabal Simone Pezzuto 27 38 0 28 Jan 2022
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 98 271 0 20 Apr 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 131 437 0 18 Dec 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 170 755 0 13 Mar 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 117 506 0 11 Mar 2020
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications E. Samaniego C. Anitescu S. Goswami Vien Minh Nguyen-Thanh Hongwei Guo Khader M. Hamdia Timon Rabczuk X. Zhuang PINN AI4CE 145 1,333 0 27 Aug 2019