An inversion problem for optical spectrum data via physics-guided machine learning

arXiv:2404.02387v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cond-mat.str-el cs.lg data distribution flexibility function glue inference machine machine learning noise novel optical physics physics.comp-ph physics.data-an robustness solve spectrum training type via