ASRNNs recover Hamiltonian dynamics and symbolic equations from trajectories with only two irregularly spaced noisy points by preserving symplectic structure without derivative estimation.
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MAL recovers correct symbolic force laws like Kepler gravity from noisy data by minimizing trajectory reconstruction, sparsity, and energy violation, reaching 100% identification via energy criterion on benchmarks.
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Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data
ASRNNs recover Hamiltonian dynamics and symbolic equations from trajectories with only two irregularly spaced noisy points by preserving symplectic structure without derivative estimation.
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Minimum-Action Learning: Energy-Constrained Symbolic Model Selection for Physical Law Identification from Noisy Data
MAL recovers correct symbolic force laws like Kepler gravity from noisy data by minimizing trajectory reconstruction, sparsity, and energy violation, reaching 100% identification via energy criterion on benchmarks.
- Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data