Machine Learning-based models in particle-in-cell codes for advanced physics extensions

Abstract

In this paper we propose a methodology for the efficient implementation of Machine Learning (ML)-based methods in particle-in-cell (PIC) codes, with a focus on Monte-Carlo or statistical extensions to the PIC algorithm. The presented approach allows for neural networks to be developed in a Python environment, where advanced ML tools are readily available to proficiently train and test them. Those models are then efficiently deployed within highly-scalable and fully parallelized PIC simulations during runtime. We demonstrate this methodology with a proof-of-concept implementation within the PIC code OSIRIS, where a fully-connected neural network is used to replace a section of a Compton scattering module. We demonstrate that the ML-based method reproduces the results obtained with the conventional method and achieves better computational performance. These results offer a promising avenue for future applications of ML-based methods in PIC, particularly for physics extensions where an ML-based approach can provide a higher performance increase.

arXiv comment12 pages, 5 figures

References in corpus (9)

• Overcoming catastrophic forgetting in neural networks
• One-to-one direct modeling of experiments and astrophysical scenarios: pushing the envelope on kinetic plasma simulations
• First-Principles Plasma Simulations of Black-Hole Jet Launching
• Quantum radiation reaction in head-on laser-electron beam interaction
• Classical Radiation Reaction in Particle-In-Cell Simulations
• Generalized, energy-conserving numerical simulations of particles in general relativity. I. Time-like and null geodesics
• Interplay between the Weibel instability and the Biermann battery in realistic laser-solid interactions
• Compton scattering in particle-in-cell codes
• Generating ultra-dense pair beams using 400 GeV/c protons

Cited by in corpus (3)

• Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines
• Learning the dynamics of a one-dimensional plasma model with graph neural networks
• QScatter: Numerical Framework for Fast Prediction of Particle Distributions in Electron-Laser Scattering