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arXiv · 1909.13334

Symplectic Recurrent Neural Networks

Zhengdao Chen·Jianyu Zhang·Martin Arjovsky·Léon Bottou
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Abstract

We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algorithms that capture the dynamics of physical systems from observed trajectories. An SRNN models the Hamiltonian function of the system by a neural network and furthermore leverages symplectic integration, multiple-step training and initial state optimization to address the challenging numerical issues associated with Hamiltonian systems. We show that SRNNs succeed reliably on complex and noisy Hamiltonian systems. We also show how to augment the SRNN integration scheme in order to handle stiff dynamical systems such as bouncing billiards.

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Zhengdao Chen, Jianyu Zhang, Martin Arjovsky, Léon Bottou. 2019-09-29. Symplectic Recurrent Neural Networks. https://arxiv.org/abs/1909.13334

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