In this talk we will showcase and explain how SciML techniques such as universal differential equations (UDEs) make it possible to improve the prediction and extrapolation capabilities of machine learning on small data.
We present pathways to model and optimize the alpha transport driven by Alfvenic instabilities through modification of the resonance structure and shear Alfven continuum. We discuss the incorporation of engineering constraints, such as HTS strain and remote maintenance compatibility, into the stellarator design process.