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 optimisation to design electron-cyclotron heating p...

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Bibliographic Details
Published in:arXiv.org 2024-05
Main Authors: Brown, Theodore, Marsden, Stephen, Gopakumar, Vignesh, Terenin, Alexander, Ge, Hong, Casson, Francis
Format: Article
Language:English
Subjects:
Bayesian analysis
Cyclotrons
Design
Design optimization
Electron cyclotron heating
Genetic algorithms
Machine learning
Multiple objective analysis
Prediction models
Safety factors
Tokamak devices
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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 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.
ISSN:2331-8422
DOI:10.48550/arxiv.2310.02669