SymFlux: deep symbolic regression of Hamiltonian vector fields

• URL: http://arxiv.org/abs/2507.06342v1
• Date: Tue, 08 Jul 2025 19:07:16 GMT
• Title: SymFlux: deep symbolic regression of Hamiltonian vector fields
• Authors: M. A. Evangelista-Alvarado, P. Suárez-Serrato,
• Abstract summary: We present SymFlux, a novel deep learning framework that performs symbolic regression to identify Hamiltonian functions from their corresponding vector fields on the standard symplectic plane.<n>Our results demonstrate the model's effectiveness in accurately recovering these symbolic expressions, advancing automated discovery in Hamiltonian mechanics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present SymFlux, a novel deep learning framework that performs symbolic regression to identify Hamiltonian functions from their corresponding vector fields on the standard symplectic plane. SymFlux models utilize hybrid CNN-LSTM architectures to learn and output the symbolic mathematical expression of the underlying Hamiltonian. Training and validation are conducted on newly developed datasets of Hamiltonian vector fields, a key contribution of this work. Our results demonstrate the model's effectiveness in accurately recovering these symbolic expressions, advancing automated discovery in Hamiltonian mechanics.

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