论文标题
Deepgraphonet：一个深图运算符网络，用于学习和零射击转移网络系统的动态响应
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems
论文作者
论文摘要
本文开发了一个深图运算符网络（Deepgraphonet）框架，该框架学会了具有基础子图形结构的复杂系统（例如电网或流量）的动力学。我们通过融合（i）图形神经网络（GNN）来利用空间相关的图形信息和（ii）深操作员网络〜（deeponet）近似动态系统的解决方案操作员的能力来构建深图载体。然后，所得的深图载体可以通过观察图态信息的有限历史来预测给定的短/中期时间范围内的动力学。此外，我们将深图载体设计为独立于解决方案。也就是说，我们不需要以精确/相同的分辨率收集有限的历史记录。此外，为了传播训练有素的Deepgraphonet的结果，我们设计了一种零射击学习策略，可以在不同的子图上使用它。最后，对（i）瞬态稳定性预测电网和（ii）车辆系统交通流量预测问题的经验结果说明了拟议的Deepgraphonet的有效性。
This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g. the power grid or traffic) with an underlying sub-graph structure. We build our DeepGraphONet by fusing the ability of (i) Graph Neural Networks (GNN) to exploit spatially correlated graph information and (ii) Deep Operator Networks~(DeepONet) to approximate the solution operator of dynamical systems. The resulting DeepGraphONet can then predict the dynamics within a given short/medium-term time horizon by observing a finite history of the graph state information. Furthermore, we design our DeepGraphONet to be resolution-independent. That is, we do not require the finite history to be collected at the exact/same resolution. In addition, to disseminate the results from a trained DeepGraphONet, we design a zero-shot learning strategy that enables using it on a different sub-graph. Finally, empirical results on the (i) transient stability prediction problem of power grids and (ii) traffic flow forecasting problem of a vehicular system illustrate the effectiveness of the proposed DeepGraphONet.
