Bayesian optimization of stellarator alpha-particle confinement using data-informed parameter spaces and dimensionality reduction
This paper proposes two data-informed parameterization methods—quantile transformation of Fourier modes and PCA-based dimensionality reduction—to enable efficient Bayesian optimization of stellarator shapes for superior alpha-particle confinement, overcoming the limitations of traditional Fourier-based approaches regarding parameter bounds and expressiveness.