标题： 物理信息EvolveGCN：多智能体系统的卫星预测 显示英文标题

标题： Physics-Informed EvolveGCN: Satellite Prediction for Multi Agent Systems

摘要： 在自主系统迅速发展的领域中，共享环境中的智能体之间的交互是不可避免且对于提升整体系统能力至关重要的。 此类多智能体系统的一个关键要求是每个智能体能够可靠地预测其最近邻居的未来位置。 传统上，图和图论一直作为建模智能体间通信和关系的有效工具。 尽管这种方法被广泛使用，但本文提出了一种新的方法，该方法以前瞻性的方式利用动态图。 具体而言，采用EvolveGCN，一种动态图卷积网络，来预测智能体间关系随时间的变化。 为了提高预测准确性并确保物理合理性，本研究结合了基于Clohessy-Wiltshire运动方程的物理约束损失函数。 这种综合方法提高了多智能体场景中未来状态估计的可靠性。 显示英文摘要

摘要： In the rapidly evolving domain of autonomous systems, interaction among agents within a shared environment is both inevitable and essential for enhancing overall system capabilities. A key requirement in such multi-agent systems is the ability of each agent to reliably predict the future positions of its nearest neighbors. Traditionally, graphs and graph theory have served as effective tools for modeling inter agent communication and relationships. While this approach is widely used, the present work proposes a novel method that leverages dynamic graphs in a forward looking manner. Specifically, the employment of EvolveGCN, a dynamic graph convolutional network, to forecast the evolution of inter-agent relationships over time. To improve prediction accuracy and ensure physical plausibility, this research incorporates physics constrained loss functions based on the Clohessy-Wiltshire equations of motion. This integrated approach enhances the reliability of future state estimations in multi-agent scenarios.