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The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a support vector machine regression (SVMR) model to predict running-in wear volume, with field surface topography parameters and working conditions as the input variables, under the condition that the amount of training samples is very limited. Experimental results proved the effectiveness of the SVMR-based model with a small amount of training samples. Based on the established prediction model, the impacts of the field surface parameters on running-in wear volume have been analyzed. The results show that Sku has the largest influence on running-in wear volume, Sdq the second, and Svk the least. [ABSTRACT FROM AUTHOR]