首页|Findings on Machine Learning Reported by Investigators at Southeast@@University ( Enhancing Classical Scheil-gulliver Model Calculations@@By Predicting Generated P hases and Corresponding Compositions@@Through Machine Learning Techniques)
Findings on Machine Learning Reported by Investigators at Southeast@@University ( Enhancing Classical Scheil-gulliver Model Calculations@@By Predicting Generated P hases and Corresponding Compositions@@Through Machine Learning Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news reportingoriginating from Nanjing, People’s R epublic of China, by NewsRx correspondents, research stated, “Theclassical Sche il-Gulliver model is an important tool for simulating non-equilibrium solidifica tion processes inmaterials science, especially for rapid cooling processes such as additive manufacturing. However, the highcomputational intensity of the Sch eil-Gulliver calculations through the CALculation of PHAse Diagrams(CALPHAD) me thod, especially for complex alloys, limits its application in high-throughput s cenarios.”
NanjingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningSoutheast University
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