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

Part of: Machine Learning for Plasma Physics and Fusion Energy

Published online by Cambridge University Press:  07 December 2022

Chiara Badiali Open the ORCID record for Chiara Badiali [Opens in a new window] ,
Pablo J. Bilbao Open the ORCID record for Pablo J. Bilbao [Opens in a new window] ,
Fábio Cruz Open the ORCID record for Fábio Cruz [Opens in a new window]  and
Luís O. Silva Open the ORCID record for Luís O. Silva [Opens in a new window]
Chiara Badiali*
Affiliation:
GoLP/Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Pablo J. Bilbao
Affiliation:
GoLP/Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Fábio Cruz
Affiliation:
GoLP/Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal Inductiva Research Labs, Rua da Prata 80, 1100-420 Lisboa, Portugal
Luís O. Silva*
Affiliation:
GoLP/Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
*
†Email addresses for correspondence: chiara.badiali@tecnico.ulisboa.pt, luis.silva@tecnico.ulisboa.pt
†Email addresses for correspondence: chiara.badiali@tecnico.ulisboa.pt, luis.silva@tecnico.ulisboa.pt
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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 a ML-based approach can provide a higher performance increase.

Keywords

plasma simulation
Type
Research Article
Information
Journal of Plasma Physics , Volume 88 , Issue 6 , December 2022 , 895880602
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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Footnotes

‡

C. Badiali and P.J. Bilbao contributed equally to this work.

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