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- W4387389985 abstract "The safety factor profile is a key property in determining the stability of tokamak plasmas. To improve the safety factor profile in the United Kingdom's proposed Spherical Tokamak for Energy Production (STEP), we apply multi-objective Bayesian optimisation - a machine learning technique in which an uncertainty-aware predictive model guides the optimisation process based on the observed data - to design electron-cyclotron heating profiles. The resulting procedure generates sets of solutions that represent optimal tradeoffs between objectives, enabling decision makers to understand the compromises made during the design process. The solutions from our method score as well as those generated in previous work by a genetic algorithm; however, our method leads to a greater degree of solution diversity and interpretability, providing more information to tokamak designers without compromising performance. Our results suggest that the region of reversed central safety factor in STEP can be reduced at the cost of letting rational safety factor surfaces move slightly inwards, while keeping the minimum safety factor above two." @default.
- W4387389985 created "2023-10-06" @default.
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- W4387389985 date "2023-10-04" @default.
- W4387389985 modified "2023-10-09" @default.
- W4387389985 title "Multi-objective Bayesian optimisation for design of Pareto-optimal current drive profiles in STEP" @default.
- W4387389985 doi "https://doi.org/10.48550/arxiv.2310.02669" @default.
- W4387389985 hasPublicationYear "2023" @default.
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