  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.
