Symplectic Taylor Neural Networks

Paper: https://arxiv.org/abs/2005.04986

Summary

We propose an effective and light-weighted learning algorithm, Symplectic Taylor Neural Networks (Taylor-nets), to conduct continuous, long-term predictions of a complex Hamiltonian dynamic system based on sparse, short-term observations. At the heart of our algorithm is a novel neural network architecture consisting of two sub-networks. Both are embedded with terms in the form of Taylor series expansion that are designed with a symmetric structure. The key mechanism underpinning our infrastructure is the strong expressiveness and special symmetric property of the Taylor series expansion, which can inherently accommodate the numerical fitting process of the spatial derivatives of the Hamiltonian as well as preserve its symplectic structure. We further incorporate a fourth-order symplectic integrator in conjunction with neural ODEs’ framework into our Taylor-net architecture to learn the continuous time evolution of the target systems while preserving their symplectic structures simultaneously.

Prerequisites

Python should be installed in order to run the program. In a newly created virtual environmnent, run the following command:

pip install -r requirements.txt

All the required dependencies will then be installed.

Usage

We demonstrated the efficacy of our Tayler-net in predicting a broad spectrum of Hamiltonian dynamic systems, including the pendulum, the Lotka-Volterra, the Kepler, and the Hénon–Heiles systems.

In order to train a Taylor-net:

Pendulum: python3 Pendulum/Pendulum.py
Lotka-Volterra: python3 Lotka_Volterra/LV.py
Hénon-Heiles: python3 Henon_Heiles/Henon_Heiles.py

Problem setups

Problems	Pendulum	Lotka-Volterra	Hénon-Heiles	Kepler
Training period	0.01	0.01	0.01	0.01
Predicting period	2π	20π	10	20π
Sample size	15	25	25	25
Epoch	100	150	100	50
Learning rate	0.002	0.003	0.001	0.001
step_size	10	10	10	10
γ	0.8	0.8	0.8	0.8
M*	8	8	12	20
Dimension of hidden layer	128	128	32	32