Learning the dynamics of a one-dimensional plasma model with graph neural networks

May 28, 2024

<jats:title>Abstract</jats:title> <jats:p>We explore the possibility of fully replacing a plasma physics kinetic simulator with a graph neural network-based simulator. We focus on this class of surrogate models given the similarity between their message-passing update mechanism and the traditional physics solver update, and the possibility of enforcing known physical priors into the graph construction and update. We show that our model learns the kinetic plasma dynamics of the one-dimensional plasma model, a predecessor of contemporary kinetic plasma simulation codes, and recovers a wide range of well-known kinetic plasma processes, including plasma thermalization, electrostatic fluctuations about thermal equilibrium, and the drag on a fast sheet and Landau damping. We compare the performance against the original plasma model in terms of run-time, conservation laws, and temporal evolution of key physical quantities. The limitations of the model are presented and possible directions for higher-dimensional surrogate models for kinetic plasmas are discussed.</jats:p>

Authors in the community:

• 

  L

  Luís Miguel De Oliveira e Silva

  ist13387

  Affiliations and sources

  IST > DF

  Departamento de Física

  IST > IPFN

  Instituto de Plasmas e Fusão Nuclear

  In: orcid

  ID: 160429699

AM - Accepted manuscript

IOP Publishing

Machine Learning: Science and Technology

025048

5

2

2632-2153

10.1088/2632-2153/ad4ba6

physical-sciences - Physical sciences

eng - English