首页|New Machine Learning Findings from Southwest Jiaotong University Described (Inte rpretable Machine Learning Method for Modelling Fatigue Short Crack Growth Behav iour)
New Machine Learning Findings from Southwest Jiaotong University Described (Inte rpretable Machine Learning Method for Modelling Fatigue Short Crack Growth Behav iour)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating from Chengdu, People's Republ ic of China, by NewsRx correspondents, research stated, "Interpretable machine l earning (ML) has become a popular tool in the field of science and engineering. This research proposed a domain knowledge combined with ML method to increase in terpretability while ensuring the accuracy of ML models and verifies the general ity of the ML approach in fatigue crack growth (FCG) modelling." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Sichuan Science and Technology Program, National Railway Administration of the P.R.C, I ndependent Research Project of State Key Laboratory of Traction Power, China Sch olarship Council.
ChengduPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningSouthwest Jiaotong Universi ty