Bayesian optimization of poloidal field coil positions in tokamaks

The tokamak is a world-leading concept for producing sustainable energy via magnetically confined nuclear fusion. Identifying where to position the magnets within a tokamak, specifically the poloidal field (PF) coils, is a design problem which requires balancing a number of competing economical, physical, and engineering objectives and constraints. In this paper, we show that multi-objective Bayesian optimization (BO), an iterative optimization technique utilizing probabilistic machine learning models, can effectively explore this complex design space and return several optimal PF coilsets. These solutions span the Pareto front, a subset of the objective space that optimally satisfies the specified objective functions. We outline an easy-to-use BO framework and demonstrate that it outperforms alternative optimization techniques while using significantly fewer computational resources. Our results show that BO is a promising technique for fusion design problems that rely on computationally demanding high-fidelity simulations.