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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2404.02387 |
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| _version_ | 1866913297018650624 |
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| author | Park, Hwiwoo Park, Jun H. Hwang, Jungseek |
| author_facet | Park, Hwiwoo Park, Jun H. Hwang, Jungseek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02387 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An inversion problem for optical spectrum data via physics-guided machine learning Park, Hwiwoo Park, Jun H. Hwang, Jungseek Data Analysis, Statistics and Probability Strongly Correlated Electrons Machine Learning Computational Physics 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. |
| title | An inversion problem for optical spectrum data via physics-guided machine learning |
| topic | Data Analysis, Statistics and Probability Strongly Correlated Electrons Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2404.02387 |