Session

DescriptionThe quest for fusion as an environmentally benign, virtually inexhaustible energy source has recently taken frontstage thanks to a number of breakthroughs such as a new world record for fusion power or the first demonstration of energetic breakeven. This minisymposium is dedicated to addressing challenges in the simulation of magnetic fusion plasmas and, more specifically, on the latest data-driven approaches, complementing the more traditional ones. These include the use of deep learning methods to control the operation of tokamaks, the application of physics-informed neural networks to accelerate the solution of the plasma kinetic equations, the development of innovative techniques to accelerate the gathering of training sets for neural surrogate models, as well as the development of neural networks that preserve the symplectic nature of the underlying equations used for performing reduced-order modelling.