首页|Study Data from Beijing Institute of Technology Provide New Insights into Machin e Learning (Machine Learning-assisted Design of Refractory High-entropy Alloys W ith Targeted Yield Strength and Fracture Strain)
Study Data from Beijing Institute of Technology Provide New Insights into Machin e Learning (Machine Learning-assisted Design of Refractory High-entropy Alloys W ith Targeted Yield Strength and Fracture Strain)
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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 Beijing, People's Republ ic of China, by NewsRx correspondents, research stated, "In order to improve the traditional ‘trial and error' material design method, machine learning-yield st rength and machine learning-fracture strain models are incorporated into one sys tem to predict yield strength and fracture strain in refractory high-entropy all oys (RHEAs) under compression. The ML-yield strength model and MLfracture strain model achieve excellent predictions (R2 = 0.942, RMSE=0.35) and (R2 = 0.892, RM SE=0.41) in the testing set, respectively." Financial supporters for this research include National Key Laboratory Foundatio n of Science and Technology on Materials, China under Shock and Impact, Overseas Young Talents Program, National Natural Science Foundation of China (NSFC), You th Academic Start-up Program at Beijing Institute of Technology, China, RCPT, Ch ina, National Natural Science Foundation of China (NSFC), National Key Laborator y Foundation of Science and Technology on Materials, China, National Natural Sci ence Foundation of China (NSFC).
BeijingPeople's Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine LearningBeijing Institute of Technology
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