2019/06/11 by Roberto Bondesan, Bondesan, Roberto, Austen Lamacraft +1 · 3 citations
Computer Science · Physics and Astronomy · #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Neural Networks and Applications #Topic Modeling #cs.LG #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.1906.04645
8 pages, 5 figures. Presented at the ICML 2019 Workshop on Theoretical Physics for Deep Learning
arxiv created 2019/06/11 · openalex publication_date 2019/06/11 · arxiv updated 2019/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The solution of problems in physics is often facilitated by a change of variables. In this work we present neural transformations to learn symmetries of Hamiltonian mechanical systems. Maintaining the Hamiltonian structure requires novel network architectures that parametrize symplectic transformations. We demonstrate the utility of these architectures by learning the structure of integrable models. Our work exemplifies the adaptation of neural transformations to a family constrained by more than the condition of invertibility, which we expect to be a common feature of applications of these methods.