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Physics informed Neural Networks applied to the description of wave-particle resonance in kinetic simulations of fusion plasmas

arXiv 2308.12312 ↗First posted 23 Aug 2023physics.comp-ph cs.AI physics.plasm-phcited by 0

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Abstract (arXiv)

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.

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References · 17

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5 more
  1. 2019Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networkscited by 64
  2. 2019NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Datacited by 42
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  4. 2022Data-driven modeling of Landau damping by physics-informed neural networkscited by 2
  5. 2022Integral Transforms in a Physics-Informed (Quantum) Neural Network setting: Applications & Use-Casescited by 1

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