Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction

arXiv:2404.06460v1 Announce Type: cross
Abstract: Locally interacting dynamical systems, such as epidemic spread, rumor propagation through crowd, and forest fire, exhibit complex global dynamics originated from local, relatively simple, and often stochastic interactions between dynamic elements. Their temporal evolution is often driven by transitions between a finite number of discrete states. Despite significant advancements in predictive modeling through deep learning, such interactions among many elements have rarely explored as a specific domain for predictive modeling. We present Attentive Recurrent Neural …

abstract arxiv cs.lg cs.sy data dynamic dynamics eess.sy epidemic evolution fire global interactions prediction propagation rumor scalable simple stochastic systems temporal through transitions type