首页|New Machine Learning Findings from Zhejiang University Dis- cussed (Machine Learning-based Prediction and Generation Model for Creep Rupture Time of Nickel-based Alloys)
New Machine Learning Findings from Zhejiang University Dis- cussed (Machine Learning-based Prediction and Generation Model for Creep Rupture Time of Nickel-based Alloys)
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NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news reporting originating from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Pre- dicting the creep rupture time of Nickel-based alloys has been one of the central topics in materials science. This work is devoted to develop a comprehensive strategy based on machine learning for data generation and prediction." Financial support for this research came from NSFC key project of joint funds. Our news editors obtained a quote from the research from Zhejiang University, "Our objective is to improve significantly the accuracy and effectiveness of the rupture time prediction. The two different prediction models, residual learning combining liner regression model with nonlinear regression model, ensemble learning combining with genetic algorithm, are employed. Both of them are shown to achieve high accuracy on the training dataset and testing dataset. To overcome the limited availability of experimental data, a novel generation model is designed based on generative adversarial network. The new model is capable of generating a batch of high-quality synthetic dataset for expanding the scale of the training dataset. In particular, the best results obtained by the model for three metrics R2, RMSE, MAE are 0.9971, 0.1259, 0.0395. The proportion of the augmented synthetic dataset is also discussed."
HangzhouPeople's Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine LearningNickelTransition ElementsZhejiang University
NETL
NSTL
Elsevier
