Multi-Objective Bayesian Optimization for Design of Pareto-Optimal Current Drive Profiles in STEP

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 optimization (MOBO) to design electron-cyclotron he...

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Bibliographic Details
Published in:IEEE transactions on plasma science 2024, Vol.52 (9), p.3904-3909
Main Authors: Brown, Theodore, Marsden, Stephen, Gopakumar, Vignesh, Terenin, Alexander, Ge, Hong, Casson, Francis
Format: Article
Language:English
Subjects:
Bayes methods
Fusion reactor design
Gaussian processes
machine learning
Optimization
optimization methods
Plasma properties
Plasmas
Safety
Tokamak devices
Vectors
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Summary: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 optimization (MOBO) to design electron-cyclotron heating profiles. Bayesian optimization (BO) 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 optimization. By taking a multi-objective approach, the optimizer 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 (GA); 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.
ISSN:0093-3813
1939-9375
DOI:10.1109/TPS.2024.3382775