What is recorded here?
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.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
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People and systems
Sam Greydanus; Misko Dzamba; Jason Yosinski
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English catalogue narrative
Hamiltonian neural networks for structured learned dynamics
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
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.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Sam Greydanus; Misko Dzamba; Jason Yosinski
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.