Reproduce “Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control” on OpenResearch

About this paper

Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

The Symplectic ODE-Net (SymODEN) framework learns the underlying ordinary differential equation dynamics of physical systems by incorporating generalized Hamiltonian dynamics as an inductive bias, achieving improved generalization with limited data and enabling robust model-based control synthesis.

Key insights

• Incorporating generalized Hamiltonian dynamics as an inductive bias significantly enhances the generalization capabilities and data efficiency of deep learning models for physical systems, especially with scarce training data.
• The framework successfully learns physically interpretable components of the Hamiltonian (e.g., inverse mass matrix, potential energy), providing transparency into the system's dynamics beyond just state prediction.
• Learning a physically consistent Hamiltonian structure enables direct synthesis of model-based control strategies, such as energy shaping, which can robustly stabilize systems even when extrapolating beyond training control magnitudes.

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