Efficient training of physics-informed neural networks via importance sampling

26 April 2021

Papers citing "Efficient training of physics-informed neural networks via importance sampling"

7 / 7 papers shown

Title
Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs Jose Florido He-Nan Wang Amirul Khan P. Jimack 21 2 0 18 Apr 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 8 16 0 05 Jan 2024
Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm Shikhar Nilabh F. Grandia 20 1 0 14 Jun 2023
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs Yuling Jiao Dingwei Li Xiliang Lu J. Yang Cheng Yuan 10 9 0 28 Mar 2023
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal Francesco Di Fiore Michela Nardelli L. Mainini 23 22 0 02 Mar 2023
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media Daniel Amini E. Haghighat R. Juanes PINN AI4CE 11 22 0 03 Mar 2022
$NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework$ NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework O. Hennigh S. Narasimhan M. A. Nabian Akshay Subramaniam Kaustubh Tangsali M. Rietmann J. Ferrandis Wonmin Byeon Z. Fang S. Choudhry PINN AI4CE 85 112 0 14 Dec 2020