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Abstract：
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.
摘要：
本文提出的元学习方法解决了小样本回归在工程数据分析中的应用问题,这是一个非常有前途的研究方向。通过将传统回归模型与元学习中基于优化的数据增强相结合，所提出的深度神经网络在优化玻璃纤维增强塑料（GFRP）包裹混凝土短柱方面表现出优异的性能。与传统回归模型相比，如支持向量回归(SVR)，高斯过程回归(GPR)，和径向基函数神经网络(RBFNN)，本文提出的元学习方法在对小数据样本进行建模时表现更好。这种方法的成功说明了深度学习在处理有限数量数据方面的潜力，在材料数据分析领域提供新的机会。
