首页|New Findings Reported from University of Lorraine Describe Advances in Machine Learning (Physics-informed Machine Learning Prediction of the Martensitic Transformation Temperature for the Design of “niti-like” High Entropy Shape Memory Alloys)
New Findings Reported from University of Lorraine Describe Advances in Machine Learning (Physics-informed Machine Learning Prediction of the Martensitic Transformation Temperature for the Design of “niti-like” High Entropy Shape Memory Alloys)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - Researchers detail new data in Machine Learning. According to news reporting originating in Metz,France, by NewsRx journalists, research stated, “The present study proposes a physics-informed machinelearning (PIML) algorithm-based approach aimed at predicting the martensitic transformation temperature(Ms) for the design of ‘NiTi-like’ high entropy shape memory alloys (HESMAs). A previously establishedHESMAs database is enriched and extended to include bi-nary, ternary, quaternary, quinary and senary alloys containing the most employed alloying elements for HEAs design such as Ni equivalents (Fe, Cu, Co,Pd, Pt and Au), Ti equivalents (Zr and Hf), Nb and Ta.”
MetzFranceEuropeAlgorithmsAlloysCyborgsEmerging TechnologiesMachine LearningUniversity of Lorraine
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