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Topological analysis of the magnetic field in simulated plasmas allows thestudy of various physical phenomena in a wide range of settings. One suchapplication is magnetic reconnection, a phenomenon related to the dynamics ofthe magnetic field topology, which is difficult to detect and characterize inthree dimensions. We propose a scalable pipeline for topological data analysisand spatiotemporal graph representation of three-dimensional magnetic vectorfields. We demonstrate our methods on simulations of the Earth's magnetosphereproduced by Vlasiator, a supercomputer-scale Vlasov theory-based simulation fornear-Earth space. The purpose of this work is to challenge the machine learningcommunity to explore graph-based machine learning approaches to address alargely open scientific problem with wide-ranging potential impact.

Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applications