首页|Researchers from Lomonosov Moscow State University Describe Findings in Machine Learning (Starkml: Application of Machine Learning To Overcome Lack of Data On Electron-impact Broadening Parameters)
Researchers from Lomonosov Moscow State University Describe Findings in Machine Learning (Starkml: Application of Machine Learning To Overcome Lack of Data On Electron-impact Broadening Parameters)
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NETL
NSTL
Oxford Univ Press
Research findings on Machine Learning are discussed in a new report. According to news originating from Moscow, Russia, by NewsRx correspondents, research stated, “Parameters of electron-impact (Stark) broadening and shift of spectral lines are of key importance in various studies of plasma spectroscopy and astrophysics. To overcome the lack of accurately known Stark parameters, we developed a machine learning approach for predicting Stark parameters of neutral atoms’ lines.” Financial support for this research came from Non-commercial Foundation for the Advancement of Science and Education INTELLECT. Our news journalists obtained a quote from the research from Lomonosov Moscow State University, “By implementing a data pre-processing routine and explicitly testing models’ predictive ability and generalizability, we achieve a high level of accuracy in parameters prediction as well as physically meaningful temperature dependence. The applicability of the results is demonstrated by the case of low-temperature plasma diagnostics.”
MoscowRussiaCyborgsEmerging TechnologiesMachine LearningLomonosov Moscow State University
NETL
NSTL
Oxford Univ Press
