Neural Graph Simulator for Complex Systems

• URL: http://arxiv.org/abs/2411.09120v1
• Date: Thu, 14 Nov 2024 01:41:00 GMT
• Title: Neural Graph Simulator for Complex Systems
• Authors: Hoyun Choi, Sungyeop Lee, B. Kahng, Junghyo Jo,
• Abstract summary: We introduce the Neural Graph Simulator (NGS) for simulating time-invariant autonomous systems on graphs. NGS offers significant advantages over numerical solvers by not requiring prior knowledge of governing equations. It demonstrates superior computational efficiency over conventional methods, improving performance by over $105$ times in stiff problems.
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• Abstract: Numerical simulation is a predominant tool for studying the dynamics in complex systems, but large-scale simulations are often intractable due to computational limitations. Here, we introduce the Neural Graph Simulator (NGS) for simulating time-invariant autonomous systems on graphs. Utilizing a graph neural network, the NGS provides a unified framework to simulate diverse dynamical systems with varying topologies and sizes without constraints on evaluation times through its non-uniform time step and autoregressive approach. The NGS offers significant advantages over numerical solvers by not requiring prior knowledge of governing equations and effectively handling noisy or missing data with a robust training scheme. It demonstrates superior computational efficiency over conventional methods, improving performance by over $10^5$ times in stiff problems. Furthermore, it is applied to real traffic data, forecasting traffic flow with state-of-the-art accuracy. The versatility of the NGS extends beyond the presented cases, offering numerous potential avenues for enhancement.

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