[2604.02756] STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation

Computer Science > Machine Learning arXiv:2604.02756 (cs) [Submitted on 3 Apr 2026] Title:STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation Authors:Zijin Liu, Xu Geng, Wenshuai Xu, Xiang Zhao, Yan Xia, You Song View a PDF of the paper titled STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation, by Zijin Liu and Xu Geng and Wenshuai Xu and Xiang Zhao and Yan Xia and You Song View PDF HTML (experimental) Abstract:Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of independent individual trajectories, are limited in their ability to capture macroscopic physical laws. This microscopic approach often leads to error accumulation and compromises simulation stability. Furthermore, deep learning-driven methods tend to suffer from low inference efficiency and high computational overhead, making them impractical for large-scale, efficient simulations. To address these challenges, we propose the Spatio-Temporal Decoupled Differential Equation Network (STDDN), a novel framework that guides microscopic trajectory prediction with macroscopic physics. We innovatively introduce the continuity equation from fluid dynamics as a strong physical constraint. A Neural Ordinary Differential Equation (Neural ODE) is employed to model the macroscopic density evolution driven by individual movements, thereb...