PINNACLE: PINN Adaptive ColLocation and Experimental points selection

PINNACLE: PINN Adaptive ColLocation and Experimental points selection

11 April 2024

Gregory Kang Ruey Lau

Apivich Hemachandra

See-Kiong Ng

Papers citing "PINNACLE: PINN Adaptive ColLocation and Experimental points selection"

7 / 7 papers shown

Title
Learning and Transferring Physical Models through Derivatives Alessandro Trenta Andrea Cossu Davide Bacciu AI4CE 29 0 0 02 May 2025
Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs Jose Florido He-Nan Wang Amirul Khan P. Jimack 26 2 0 18 Apr 2024
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 56 58 0 23 May 2022
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search Yao Shu Zhongxiang Dai Zhaoxuan Wu K. H. Low AI4CE 39 26 0 24 Jan 2022
Deep Active Learning by Leveraging Training Dynamics Haonan Wang Wei Huang Ziwei Wu A. Margenot Hanghang Tong Jingrui He AI4CE 25 31 0 16 Oct 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 69 218 0 26 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 129 435 0 18 Dec 2020