AWhat changed
Learns a scalar Hamiltonian-like function and obtains state dynamics through its symplectic gradient, fitting observed derivatives while preserving the learned quantity in the ideal continuous flow.
Scientific scope
Learns a scalar Hamiltonian-like function and obtains state dynamics through its symplectic gradient, fitting observed derivatives while preserving the learned quantity in the ideal continuous flow. Requires suitable canonical coordinates and well-defined flow. Exact conservation concerns learned Hamiltonian, not automatically true physical energy; numerical Runge–Kutta integration adds error. Derivative-target training is not label-free recovery of arbitrary laws, and conservation alone does not establish physical time-reversal symmetry or arbitrary dissipative-system validity.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Requires suitable canonical coordinates and well-defined flow. Exact conservation concerns learned Hamiltonian, not automatically true physical energy; numerical Runge–Kutta integration adds error. Derivative-target training is not label-free recovery of arbitrary laws, and conservation alone does not establish physical time-reversal symmetry or arbitrary dissipative-system validity.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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