  Spherical tokamaks (STs) have many desirable features that make them a
suitable choice for fusion power plants. To understand their confinement
properties, accurate calculation of turbulent micro-instabilities is necessary
for tokamak design. Presented is a novel surrogate model for Micro-tearing
modes (MTMs), the micro-instability thought to be dominant in high beta STs.
Direct numerical calculation of micro-instabilities is computationally
expensive and is a significant bottleneck in integrated plasma modelling. The
considerable number of geometric and thermodynamic parameters, the interactions
that influence these coefficients and the resolutions needed to accurately
resolve these modes, makes direct numerical simulation for parameter space
exploration computationally extremely challenging. However, this and the dearth
of accurate reduced physics models for MTMs makes it suitable for surrogate
modelling using Gaussian Process Regression, a modern machine learning
technique. This paper outlines the further development of a data-driven
reduced-order model across a spherical tokamak reactor-relevant parameter space
utilising Gaussian Process Regression (GPR) and classification; techniques from
machine learning. To build the original simple GP model these two components
were used in an active learning loop to maximise the efficiency of data
acquisition thus minimising computational cost. The `simple' GP was seen to
show a plateau of fidelity with more data and to be under-confident, particular
in areas of parameter space close to marginal stability. It is postulated that
the presence of multiple sub-types of MTM could be the root cause, with the
underlying function being less smooth than expected. An expansion of the model
using clustering algorithms to find optimal sub models using a mixture of
experts approach is shown to greatly improve the variances in the outputs of
the GP model.
