Physics informed Neural Networks applied to the description of wave-particle resonance in kinetic simulations of fusion plasmas
This work addresses simulation challenges in fusion plasma physics, representing an incremental improvement by adapting PINNs to kinetic models.
The study applied Physics Informed Neural Networks (PINNs) to model wave-particle resonance in fusion plasmas using the Vlasov-Poisson system, testing them on Landau damping and bump-on-tail instability cases, and introduced an Integrable PINN variant to handle integral equations.
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.