An inversion problem for optical spectrum data via physics-guided machine learning
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Abstract (arXiv)
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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References · 6
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2016Learning to learn by gradient descent by gradient descentcited by 616
- 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
- 2017Solving ill-posed inverse problems using iterative deep neural networkscited by 145
- 2017Recurrent Inference Machines for Solving Inverse Problemscited by 33
- 2021TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problemscited by 10
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