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

• URL: http://arxiv.org/abs/2404.02387v1
• Date: Wed, 3 Apr 2024 01:02:06 GMT
• Title: An inversion problem for optical spectrum data via physics-guided machine learning
• Authors: Hwiwoo Park, Jun H. Park, Jungseek Hwang,
• Abstract summary: We propose the regularized recurrent inference machine (rRIM) to solve the 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 first kind.

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