Neural Graph Simulator for Complex Systems
This addresses the problem of computational limitations in simulating complex systems for researchers and practitioners, offering a versatile and efficient alternative to traditional numerical methods.
The paper tackles the computational intractability of large-scale simulations in complex systems by introducing the Neural Graph Simulator (NGS), which improves performance by over 10^5 times in stiff problems and achieves state-of-the-art accuracy in traffic flow forecasting.
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.