Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe

22 October 2019

Papers citing "Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe"

7 / 7 papers shown

Title
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery Yana Lishkova P. Scherer Steffen Ridderbusch M. Jamnik Pietro Lio' Sina Ober-Blobaum Christian Offen PINN 73 7 0 28 Jan 2025
Approximation of nearly-periodic symplectic maps via structure-preserving neural networks Valentin Duruisseaux J. Burby Q. Tang 30 11 0 11 Oct 2022
Lagrangian Density Space-Time Deep Neural Network Topology B. Bishnoi PINN 22 1 0 30 Jun 2022
Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides Ming Zhong Jason D Miller Mauro Maggioni 14 1 0 26 Aug 2021
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 133 424 0 10 Mar 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 149 220 0 29 Sep 2019
Learning Symbolic Physics with Graph Networks M. Cranmer Rui Xu Peter W. Battaglia S. Ho PINN AI4CE 188 84 0 12 Sep 2019