Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

This paper presents a distance-preserving machine learning approach for making uncertainty-aware predictions of accelerator capacitance. The method preserves geometric relationships in the feature space while providing calibrated uncertainty estimates.

Machine Learning: Science and Technology, Volume 5, Number 4, Article 045009