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https://doi.org/10.2139/ssrn.4...
Article . 2023 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2023
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Preprint . 2023
Data sources: DBLP
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Physics Informed Neural Networks Applied to the Description of Wave-Particle Resonance in Kinetic Simulations of Fusion Plasmas

Authors: Jai Kumar; David Zarzoso; Virginie Grandgirard; Jan Ebert; Stefan Kesselheim;

Physics Informed Neural Networks Applied to the Description of Wave-Particle Resonance in Kinetic Simulations of Fusion Plasmas

Abstract

The Vlasov-Poisson system is employed in its reduced form version (1D1V) as a test bed for the applicability of Physics Informed Neural Network (PINN) to the wave-particle resonance. Two examples are explored: the Landau damping and the bump-on-tail instability. PINN is first tested as a compression method for the solution of the Vlasov-Poisson system and compared to the standard neural networks. Second, the application of PINN to solving the Vlasov-Poisson system is also presented with the special emphasis on the integral part, which motivates the implementation of a PINN variant, called Integrable PINN (I-PINN), based on the automatic-differentiation to solve the partial differential equation and on the automatic-integration to solve the integral equation.

Keywords

Plasma Physics (physics.plasm-ph), FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, FOS: Physical sciences, Computational Physics (physics.comp-ph), Physics - Computational Physics, Physics - Plasma Physics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
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