Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance
  Dynamics in Tokamak Fusion Reactors
Allen M. Wang Darren T. Garnier Cristina Rea
While fusion reactors known as tokamaks hold promise as a firm energy source,
advances in plasma control, and handling of events where control of plasmas is
lost, are needed for them to be economical. A significant bottleneck towards
applying more advanced control algorithms is the need for better plasma
simulation, where both physics-based and data-driven approaches currently fall
short. The former is bottle-necked by both computational cost and the
difficulty of modelling plasmas, and the latter is bottle-necked by the
relative paucity of data. To address this issue, this work applies the neural
ordinary differential equations (ODE) framework to the problem of predicting a
subset of plasma dynamics, namely the coupled plasma current and internal
inductance dynamics. As the neural ODE framework allows for the natural
inclusion of physics-based inductive biases, we train both physics-based and
neural network models on data from the Alcator C-Mod fusion reactor and find
that a model that combines physics-based equations with a neural ODE performs
better than both existing physics-motivated ODEs and a pure neural ODE model.