Predicting plasma evolution within a Tokamak reactor is crucial to realizing
the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal
evolution of plasma rapidly and accurately allow us to quickly iterate over
design and control strategies on current Tokamak devices and future reactors.
Modelling plasma evolution using numerical solvers is often expensive,
consuming many hours on supercomputers, and hence, we need alternative
inexpensive surrogate models. We demonstrate accurate predictions of plasma
evolution both in simulation and experimental domains using deep learning-based
surrogate modelling tools, viz., Fourier Neural Operators (FNO). We show that
FNO has a speedup of six orders of magnitude over traditional solvers in
predicting the plasma dynamics simulated from magnetohydrodynamic models, while
maintaining a high accuracy (MSE $\approx$ $10^{-5}$). Our modified version of
the FNO is capable of solving multi-variable Partial Differential Equations
(PDE), and can capture the dependence among the different variables in a single
model. FNOs can also predict plasma evolution on real-world experimental data
observed by the cameras positioned within the MAST Tokamak, i.e., cameras
looking across the central solenoid and the divertor in the Tokamak. We show
that FNOs are able to accurately forecast the evolution of plasma and have the
potential to be deployed for real-time monitoring. We also illustrate their
capability in forecasting the plasma shape, the locations of interactions of
the plasma with the central solenoid and the divertor for the full duration of
the plasma shot within MAST. The FNO offers a viable alternative for surrogate
modelling as it is quick to train and infer, and requires fewer data points,
while being able to do zero-shot super-resolution and getting high-fidelity
solutions.

使用深度学习为基础的 FNO 进行代理模拟，能够准确预测等离子体的演化，包括模拟和实际观测数据，以及在 Tokamak 反应堆中的实时监测。