Learning Symbolic Physics with Graph Networks

Learning Symbolic Physics with Graph Networks

12 September 2019

Peter W. Battaglia

Papers citing "Learning Symbolic Physics with Graph Networks"

16 / 16 papers shown

Title
Decomposing heterogeneous dynamical systems with graph neural networks Cédric Allier Magdalena C. Schneider Michael Innerberger Larissa Heinrich J. Bogovic S. Saalfeld CML AI4CE 38 0 0 27 Jul 2024
Symbolic Regression for Beyond the Standard Model Physics Shehu AbdusSalam Steve Abel M. Romão 27 5 0 28 May 2024
Machine Learning for Partial Differential Equations Steven L. Brunton J. Nathan Kutz AI4CE 42 20 0 30 Mar 2023
The transformative potential of machine learning for experiments in fluid mechanics Ricardo Vinuesa Steven L. Brunton B. McKeon AI4CE 32 68 0 28 Mar 2023
A Generalization of ViT/MLP-Mixer to Graphs Xiaoxin He Bryan Hooi T. Laurent Adam Perold Yann LeCun Xavier Bresson 47 88 0 27 Dec 2022
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression Zhongtian Dong K. Kong Konstantin T. Matchev Katia Matcheva 46 13 0 15 Nov 2022
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning Abdulhakim Alnuqaydan S. Gleyzer Harrison B. Prosper 18 14 0 17 Jun 2022
Simulating Liquids with Graph Networks Jonathan Klimesch Philipp Holl Nils Thuerey GNN AI4CE 24 8 0 14 Mar 2022
Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networks Peerasait Prachaseree Emma Lejeune PINN AI4CE 33 11 0 03 Feb 2022
Logic Explained Networks Gabriele Ciravegna Pietro Barbiero Francesco Giannini Marco Gori Pietro Lió Marco Maggini S. Melacci 37 69 0 11 Aug 2021
Entropy-based Logic Explanations of Neural Networks Pietro Barbiero Gabriele Ciravegna Francesco Giannini Pietro Lió Marco Gori S. Melacci FAtt XAI 25 78 0 12 Jun 2021
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence G. Marcus VLM 32 353 0 14 Feb 2020
Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe H. Qin 24 30 0 22 Oct 2019
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 241 438 0 01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 280 1,400 0 01 Dec 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 253 3,239 0 24 Nov 2016