.png){kind=icon}
Return
An inversion problem for optical spectrum data via physics-guided machine learning
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-59594-3.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
TIKHONOV REGULARIZATION
SUPERCONDUCTIVITY
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
27.9W
Citations:
83.5W
Organization
Cited Papers
ShareSaveA Fluorometric Assay for DNA Cleavage Reactions Characterized with BamHI Restriction EndonucleaseOAAI
OAAIShareSaveShareSavea-Axis optical conductivity of detwinned ortho-II YBa2Cu3O6.50 -: art. no. 014508.png)
PHYSICAL REVIEW BOAAI
PHYSICAL REVIEW BIF3.7
OAAIShareSaveShareSaveShareSaveShareSaveElectron-boson spectral density functions of cuprates obtained from optical spectra via machine learningPHYSICAL REVIEW B
OAAI
PHYSICAL REVIEW BIF3.7
OAAIShareSaveOptical determination of the relation between the electron-boson coupling function and the critical temperature in high-Tc cupratesPHYSICAL REVIEW BOAAI
PHYSICAL REVIEW BIF3.7
OAAIShareSave
View more