Yifan Fei

Yongjia Xu, Xinzheng Lu, Yifan Fei et al. · tsinghua

An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection.

Yongjia Xu, Xinzheng Lu, Yifan Fei et al. · tsinghua

There are numerous advantages of deep neural network surrogate modeling for response time-history prediction. However, due to the high cost of refined numerical simulations and actual experiments, the lack of data has become an unavoidable bottleneck in practical applications. An iterative self-transfer learningmethod for training neural networks based on small datasets is proposed in this study. A new mapping-based transfer learning network, named as deep adaptation network with three branches for regression (DAN-TR), is proposed. A general iterative network training strategy is developed by coupling DAN-TR and the pseudo-label strategy, and the establishment of corresponding datasets is also discussed. Finally, a complex component is selected as a case study. The results show that the proposed method can improve the model performance by near an order of magnitude on small datasets without the need of external labeled samples,well behaved pre-trainedmodels, additional artificial labeling, and complex physical/mathematical analysis.