A surrogate model with data augmentation and deep transfer learning for temperature field prediction of heat source layout

Recently, more attention has been focused on the surrogate model using deep learning methods due to its powerful representational ability. However, it is usually challenging to obtain sufficient labeled samples for effective training because of the time-consuming numerical calculation, especially for the temperature field prediction of heat source layout (HSL-TFP). This work develops a novel and effective training method to overcome this problem for the deep surrogate model in HSL-TFP. First, the prediction of temperature field is modeled as an image-to-image regression problem, and the feature pyramid network (FPN) is chosen as the backbone of the deep network. Then, considering the inter-sample difference and the limited number of training samples, pairwise temperature field difference is utilized as data augmentation to train the surrogate model. Finally, deep transfer learning is introduced to take advantage of the valuable information from similar tasks and accelerate learning among different HSL-TFP problems. Experiments employing physic simulation data are conducted to validate the effectiveness of the proposed method. The results demonstrate that the proposed methods have significantly improved the prediction precision of the deep surrogate model with a small sample.