@inproceedings{
huang2023tango,
title={{TANGO}: Time-reversal Latent Graph{ODE} for Multi-Agent Dynamical Systems},
author={Zijie Huang and Wanjia Zhao and Jingdong Gao and Ziniu Hu and Xiao Luo and Yadi Cao and Yuanzhou Chen and Yizhou Sun and Wei Wang},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=8AYetbupoZ}
}
Learning complex multi-agent system dynamics from data is crucial across many domains like physical simulations and material modeling. Existing physics-informed approaches, like Hamiltonian Neural Network, introduce inductive bias by strictly following energy conservation law. However, many real-world systems do not strictly conserve energy. Thus, we focus on Time-Reversal Symmetry, a broader physical principle indicating that system dynamics should remain invariant when time is reversed. This principle not only preserves energy in conservative systems but also serves as a strong inductive bias for non-conservative, reversible systems.
In this paper, we propose a simple-yet-effective self-supervised regularization term as a soft constraint that aligns the forward and backward trajectories predicted by a continuous graph neural network-based ordinary differential equation (GraphODE). 
In addition, we theoretically show that our regularization essentially minimizes higher-order Taylor expansion terms during the ODE integration steps, which enables our model to be more noise-tolerant and even applicable to irreversible systems.
TANGO: Time-reversal Latent GraphODE for Multi-Agent Dynamical Systems
Zijie Huang Wanjia Zhao Jingdong Gao Ziniu Hu Xiao Luo Yadi Cao Yuanzhou Chen Yizhou Sun Wei Wang