Data-Driven Discovery of Conservation Laws from Trajectories via Neural Deflation
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Abstract (arXiv)
In an earlier work by a subset of the present authors, the method of the so-called neural deflation was introduced towards identifying a complete set of functionally independent conservation laws of a nonlinear dynamical system. Here, we extend by a significant step this proposal. Instead of using the explicit knowledge of the underlying equations of motion, we develop the method directly from system trajectories. This is crucial towards enhancing the practical implementation of the method in scenarios where solely data reflecting discrete snapshots of the system are available. We showcase the results of the method and the number of associated conservation laws obtained in a diverse range of examples including 1D and 2D harmonic oscillators, the Toda lattice, the Fermi-Pasta-Ulam-Tsingou lattice and the Calogero-Moser system.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 7 Oct 2024.
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17 of 48 references.
References · 17
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5 more
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.