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arXiv 2107.02232physics.plasm-phcs.LGphysics.comp-ph
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A Deep Learning-Based Particle-in-Cell Method for Plasma Simulations

KTH Royal Institute of Technology(KTH皇家理工学院)

Xavier Aguilar, Stefano Markidis

英文摘要:

We design and develop a new Particle-in-Cell (PIC) method for plasma simulations using Deep-Learning (DL) to calculate the electric field from the electron phase space. We train a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN) to solve the two-stream instability test. We verify that the DL-based MLP PIC method produces the correct results using the two-stream instability: the DL-based PIC provides the expected growth rate of the two-stream instability. The DL-based PIC does not conserve the total energy and momentum. However, the DL-based PIC method is stable against the cold-beam instability, affecting traditional PIC methods. This work shows that integrating DL technologies into traditional computational methods is a viable approach for developing next-generation PIC algorithms.

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