  We propose the regularized recurrent inference machine (rRIM), a novel
machine-learning approach to solve the challenging problem of deriving the
pairing glue function from measured optical spectra. The rRIM incorporates
physical principles into both training and inference and affords noise
robustness, flexibility with out-of-distribution data, and reduced data
requirements. It effectively obtains reliable pairing glue functions from
experimental optical spectra and yields promising solutions for similar inverse
problems of the Fredholm integral equation of the first kind.
