Minisymposium Presentation

Plasma physics simulations for fusion are particularly demanding in terms of computing time and memory consumption. Learning methods offer a way to reduce these costs, but at the expense of losing theoretical guarantees and explainability. To mitigate these shortcomings, we propose exploring hybrid numerical methods and models that combine classical approaches on the one hand and learning-based terms on the other. We apply these approaches to accelerate simulations of the Vlasov equation.We will present reduced modeling techniques for Vlasov-Poisson simulations in a weakly collisional regime, where we use an Euler equations solver coupled with a neural network model that describes the non-equilibrium part of the kinetic distribution. Through examples, we will demonstrate the neural network model's ability to capture the missing term while also discussing the limitations of these approaches.Finally, we will briefly introduce new ideas for hybrid neural network solvers and PIC codes to move toward more cost-effective simulations.