Competitive Physics Informed Networks

23 April 2022

Papers citing "Competitive Physics Informed Networks"

8 / 8 papers shown

Title
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning Nilo Schwencke Cyril Furtlehner 74 1 0 14 Dec 2024
Unsupervised Representation Learning from Sparse Transformation Analysis Yue Song Thomas Anderson Keller Yisong Yue Pietro Perona Max Welling DRL 36 0 0 07 Oct 2024
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology Qingsong Xu Yilei Shi Jonathan Bamber Ye Tuo Ralf Ludwig Xiao Xiang Zhu AI4CE 20 10 0 08 Oct 2023
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving Junjun Yan Xinhai Chen Zhichao Wang Enqiang Zhou Jie Liu PINN AI4CE 29 1 0 12 Jul 2023
Achieving High Accuracy with PINNs via Energy Natural Gradients Johannes Müller Marius Zeinhofer 13 5 0 25 Feb 2023
Simulation Intelligence: Towards a New Generation of Scientific Methods Alexander Lavin D. Krakauer Hector Zenil Justin Emile Gottschlich Tim Mattson ... A. Hanuka Manuela Veloso Samuel A. Assefa Stephan Zheng Avi Pfeffer 40 105 0 06 Dec 2021
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning Ziming Liu Yunyue Chen Yuanqi Du Max Tegmark PINN AI4CE 40 22 0 28 Sep 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 71 223 0 26 Apr 2021