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Title: Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Paper content:
In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system from observed state trajectories.
To achieve better generalization with fewer training samples, SymODEN incorporates appropriate inductive bias by designing the associated computation graph in a physics-informed manner.
In particular, we enforce Hamiltonian dynamics with control to learn the underlying dynamics in a transparent way which can then be leveraged to draw insight about relevant physical aspects of the system, such as mass and potential energy.
In addition, we propose a parametrization which can enforce this Hamiltonian formalism even when the generalized coordinate data is embedded in a high-dimensional space or we can only access velocity data instead of generalized momentum.
This framework, by offering interpretable, physically-consistent models for physical systems, opens up new possibilities for synthesizing model-based control strategies.
In recent years, deep neural networks (Goodfellow et al., 2016) have become very accurate and widely used in many application domains, such as image recognition (He et al., 2016) , language comprehension (Devlin et al., 2019) , and sequential decision making (Silver et al., 2017) .
To learn underlying patterns from data and enable generalization beyond the training set, the learning approach incorporates appropriate inductive bias (Haussler, 1988; Baxter, 2000) by promoting representations which are simple in some sense.
It typically manifests itself via a set of assumptions, which in turn can guide a learning algorithm to pick one hypothesis over another.
The success in predicting an outcome for previously unseen data then depends on how well the inductive bias captures the ground reality.
Inductive bias can be introduced as the prior in a Bayesian model, or via the choice of computation graphs in a neural network.
In a variety of settings, especially in physical systems, wherein laws of physics are primarily responsible for shaping the outcome, generalization in neural networks can be improved by leveraging underlying physics for designing the computation graphs.
Here, by leveraging a generalization of the Hamiltonian dynamics, we develop a learning framework which exploits the underlying physics in the associated computation graph.
Our results show that incorporation of such physics-based inductive bias offers insight about relevant physical properties of the system, such as inertia, potential energy, total conserved energy.
These insights, in turn, enable a more accurate prediction of future behavior and improvement in out-of-sample behavior.
Furthermore, learning a physically-consistent model of the underlying dynamics can subsequently enable usage of model-based controllers which can provide performance guarantees for complex, nonlinear systems.
In particular, insight about kinetic and potential energy of a physical system can be leveraged to synthesize appropriate control strategies, such as the method of controlled Lagrangian (Bloch et al., 2001 ) and interconnection & damping assignment (Ortega et al., 2002) , which can reshape the closed-loop energy landscape to achieve a broad range of control objectives (regulation, tracking, etc.) .
Here we have introduced Symplectic ODE-Net which provides a systematic way to incorporate prior knowledge of Hamiltonian dynamics with control into a deep learning framework.
We show that SymODEN achieves better prediction with fewer training samples by learning an interpretable, physically-consistent state-space model.
Future works will incorporate a broader class of physicsbased prior, such as the port-Hamiltonian system formulation, to learn dynamics of a larger class of physical systems.
SymODEN can work with embedded angle data or when we only have access to velocity instead of generalized momentum.
Future works would explore other types of embedding, such as embedded 3D orientations.
Another interesting direction could be to combine energy shaping control (potential as well as kinetic energy shaping) with interpretable end-to-end learning frameworks.
Tianshu Wei, Yanzhi Wang, and Qi Zhu.
Deep Reinforcement Learning for Building HVAC Control.
In Proceedings of the 54th Annual Design Automation Conference (DAC), pp. 22:1-22:6, 2017.

assistant: text: This work enforces Hamiltonian dynamics with control to learn system models from embedded position and velocity data, and exploits this physically-consistent dynamics to synthesize model-based control via energy shaping.