A Deep Learning-Based Particle-in-Cell Method for Plasma Simulations

Aguilar, Xavier

doi:10.1109/cluster48925.2021.00103

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…However, as the force acting on each particle requires the electric field, evaluation of the first derivative implied significant distortion of the long-term spatiotemporal dynamics. This finding has been corroborated by Aguilar et al 26 who have reported similar findings using fully connected ANNs [a.k.a. multilayer perceptrons (MLPs) 27 ] and convolutional neural networks (CNNs).…”

Section: Data-driven Surrogate Sub-modelingsupporting

confidence: 86%

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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Machine learning has had an enormous impact in many scientific disciplines. It has also attracted significant interest in the field of low-temperature plasma (LTP) modeling and simulation in past years. Its application should be carefully assessed in general, but many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and datadriven modeling. In this survey, we approach two main objectives: (a) we review the state-of-the-art, focusing on approaches to LTP modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data.

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Section: Data-driven Surrogate Sub-modelingsupporting

confidence: 86%

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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“…Recent works on assisting plasma kinetic simulations (Aguilar & Markidis 2021;Kube et al 2021) with ML-based methods offer promising prospects for efficiently solving challenging substeps of the PIC algorithm. It is thus expected that ML-based methods and plasma simulations will be used in tandem in the near future.…”

Section: The Ml-pic Interfacementioning

confidence: 99%

“…Machine learning (ML) offers a promising avenue for the discovery of new algorithms that could facilitate the inclusion of advanced physics modules in PIC models via, for example, generalizable approximators of unknown functions or new solvers of partial differential equations. Early works incorporating ML-based methods in the PIC loop focused on assisting (Kube, Churchill & Sturdevant 2021) and replacing (Aguilar & Markidis 2021) the numerical solver of Maxwell's equations, showing that its accuracy is preserved.…”

mentioning

confidence: 99%

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

Badiali

Bilbao²,

Cruz³

et al. 2022

J. Plasma Phys.

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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.

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“…To obtain computational speed-ups, there have been recent attempts to combine existing PIC codes with machine learning surrogate models. These efforts include approaches to accelerate [5] or fully replace [6,7] the field solver block, reduce the computational burden associated with the particle push and grid-particle/particle-grid interpolation [8,9], and the integration of surrogate models into advanced physics extensions [10]. In parallel, PIC simulations and machine learning algorithms have also been used to train fast surrogate models for plasma accelerator setups [11][12][13][14], to learn closures for fluid simulations [15], to model hybrid plasma representations [16], and to recover reduced plasma models [17].…”

Section: Introductionmentioning

confidence: 99%

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

Carvalho,

Ferreira,

Silva

2024

Mach. Learn.: Sci. Technol.

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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.

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A Deep Learning-Based Particle-in-Cell Method for Plasma Simulations

Cited by 8 publications

References 16 publications

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Review: Machine learning for advancing low-temperature plasma modeling and simulation

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

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

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