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

• URL: http://arxiv.org/abs/2307.09469v2
• Date: Wed, 26 Jul 2023 10:03:53 GMT
• Title: Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applications
• Authors: Ioanna Bouri, Fanni Franssila, Markku Alho, Giulia Cozzani, Ivan Zaitsev, Minna Palmroth, Teemu Roos
• Abstract summary: Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings. Magnetic reconnection, a phenomenon related to the dynamics of the magnetic field topology, is difficult to detect and characterize in three dimensions. We propose a pipeline for scalable data analysis and a graph representation of three magnetic fields.
• Score: 0.7650548154518115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings. One such application is magnetic reconnection, a phenomenon related to the dynamics of the magnetic field topology, which is difficult to detect and characterize in three dimensions. We propose a scalable pipeline for topological data analysis and spatiotemporal graph representation of three-dimensional magnetic vector fields. We demonstrate our methods on simulations of the Earth's magnetosphere produced by Vlasiator, a supercomputer-scale Vlasov theory-based simulation for near-Earth space. The purpose of this work is to challenge the machine learning community to explore graph-based machine learning approaches to address a largely open scientific problem with wide-ranging potential impact.

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