AI Poincaré 2.0: Machine Learning Conservation Laws from
Differential Equations

AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations

23 March 2022

Papers citing "AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations"

16 / 16 papers shown

Title
AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge You-Le Fang Dong-Shan Jian Xiang Li Yan Ma 23 0 0 02 Apr 2025
Interpretable Machine Learning in Physics: A Review Sebastian Johann Wetzel Seungwoong Ha Raban Iten Miriam Klopotek Ziming Liu AI4CE 75 0 0 30 Mar 2025
Learning finite symmetry groups of dynamical systems via equivariance detection Pablo Calvo-Barlés Sergio G. Rodrigo Luis Martín-Moreno 47 0 0 04 Mar 2025
Data-Driven Discovery of Conservation Laws from Trajectories via Neural Deflation Shaoxuan Chen Panayotis G. Kevrekidis Hong-Kun Zhang Wei Zhu PINN 16 1 0 07 Oct 2024
Exploring the Truth and Beauty of Theory Landscapes with Machine Learning Konstantin T. Matchev Katia Matcheva Pierre Ramond Sarunas Verner 14 2 0 21 Jan 2024
A charge-preserving method for solving graph neural diffusion networks Lidia Aceto Pietro Antonio Grassi 12 0 0 16 Dec 2023
Seeking Truth and Beauty in Flavor Physics with Machine Learning Konstantin T. Matchev Katia Matcheva Pierre Ramond Sarunas Verner AI4CE 11 2 0 31 Oct 2023
Discovering New Interpretable Conservation Laws as Sparse Invariants Ziming Liu Patrick Obin Sturm Saketh Bharadwaj Sam Silva M. Tegmark 10 6 0 31 May 2023
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction Wang Zhang Tsui-Wei Weng Subhro Das Alexandre Megretski Lucani E. Daniel Lam M. Nguyen PINN 8 1 0 11 Feb 2023
Model-agnostic machine learning of conservation laws from data Shivam Arora Alexander Bihlo Rudiger Brecht P. Holba PINN AI4CE 13 3 0 12 Jan 2023
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression Zhongtian Dong K. Kong Konstantin T. Matchev Katia Matcheva 17 13 0 15 Nov 2022
Discovering Conservation Laws using Optimal Transport and Manifold Learning Peter Y. Lu Rumen Dangovski M. Soljavcić 16 17 0 31 Aug 2022
Learning quantum symmetries with interactive quantum-classical variational algorithms Jonathan Z. Lu R. A. Bravo Kaiying Hou Gebremedhin A. Dagnew S. Yelin K. Najafi 14 3 0 23 Jun 2022
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning Ziming Liu Yunyue Chen Yuanqi Du Max Tegmark PINN AI4CE 35 22 0 28 Sep 2021
Machine-learning hidden symmetries Ziming Liu Max Tegmark 40 52 0 20 Sep 2021
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics D. Kunin Javier Sagastuy-Breña Surya Ganguli Daniel L. K. Yamins Hidenori Tanaka 97 77 0 08 Dec 2020