SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification

• URL: http://arxiv.org/abs/2408.16698v1
• Date: Thu, 29 Aug 2024 16:47:58 GMT
• Title: SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification
• Authors: Alan John Varghese, Zhen Zhang, George Em Karniadakis,
• Abstract summary: Symplectic Graph Neural Networks (SympGNNs) can effectively handle system identification in high-dimensional Hamiltonian systems. We show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.
• Score: 4.275204859038151
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combines symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: i) G-SympGNN and ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. We also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

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