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Hamiltonian mechanics

topic1 events
papersTODAY 04:00 UTC

Symplectic Neural Networks Target Non-Separable Hamiltonian Systems

A revised arXiv paper proposes a symplectic neural network approach for learning non-separable Hamiltonians directly from noisy state observations. Hamiltonian Neural Networks embed physical priors by learning a system's energy function, which can improve generalization and reduce data needs compared with standard models. The work focuses on extending this to systems whose Hamiltonians cannot be split into kinetic and potential parts.

research/physics/neural-networks/arxiv