首页|Researchers from Dalian University of Technology Report Details of New Studies and Findings in the Area of Machine Learning (A General Scheme for Point Defect Sink Strength Calculation and Related Machine-learning-based Expressions)
Researchers from Dalian University of Technology Report Details of New Studies and Findings in the Area of Machine Learning (A General Scheme for Point Defect Sink Strength Calculation and Related Machine-learning-based Expressions)
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NETL
NSTL
Elsevier
Investigators discuss new findings in Machine Learning. According to news originating from Dalian, People’s Republic of China, by NewsRx correspondents, research stated, “Irradiation tends to increase the concentration of point defects (PDs) in crystalline materials, whose consecutive interactions with other types of defects, such as dislocation and void, are recognised highly responsible for the characteristic plastic and damaging behaviours of materials under irradiation. Conventional treatments on evaluating the strength of PD sinks see their limitation with strong regularity requirements over the models used for summarising the key underlying microstructural behaviours, where analytical solutions are bound to be the outcome.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities.
DalianPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningDalian University of Technology
NETL
NSTL
Elsevier
