Stretched and measured neural predictions of complex network dynamics

arXiv:2301.04900v4 Announce Type: replace-cross
Abstract: Differential equations are a ubiquitous tool to study dynamics, ranging from physical systems to complex systems, where a large number of agents interact through a graph with non-trivial topological features. Data-driven approximations of differential equations present a promising alternative to traditional methods for uncovering a model of dynamical systems, especially in complex systems that lack explicit first principles. A recently employed machine learning tool for studying dynamics is neural networks, which can be used for …

abstract agents alternative arxiv complex systems cond-mat.stat-mech cs.lg cs.si data data-driven differential dynamics features graph network predictions stat.ml study systems through tool type