  The safety factor profile is a key property in determining the stability of
tokamak plasmas. To design the safety factor profile in the United Kingdom's
proposed Spherical Tokamak for Energy Production (STEP), we apply
multi-objective Bayesian optimisation to design electron-cyclotron heating
profiles. Bayesian optimisation is an iterative machine learning technique that
uses an uncertainty-aware predictive model to choose the next designs to
evaluate based on the data gathered during optimisation. By taking a
multi-objective approach, the optimiser generates sets of solutions that
represent optimal tradeoffs between objectives, enabling decision makers to
understand the compromises made in each design. The solutions from our method
score higher than those generated in previous work by a genetic algorithm;
however, the key result is that our method returns a purposefully diverse range
of optimal solutions, providing more information to tokamak designers without
incurring additional computational cost.
