Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network

Temperature field prediction is of great importance in the thermal design of systems engineering, and building a surrogate model is an effective method for the task. Ensuring a high prediction performance for the surrogate models, especially deep learning models with high representational power and numerous parameters, typically requires a significant amount of labeled data. However, obtaining labeled data, particularly high-fidelity data can be prohibitively expensive. To solve this problem, this paper proposes a novel deep multi-fidelity modeling method for temperature field prediction, which takes advantage of low-fidelity data to boost performance with less high-fidelity data. First, a pithy pre-train and fine-tune paradigm is proposed for constructing the deep multi-fidelity model, which is straightforward and efficient, allowing for the effective utilization of information from various fidelity levels. Then, a physics-driven self-supervised learning method is proposed to learn the deep multi-fidelity model, which fully utilizes the physics characteristics of the heat transfer system and further reduces the dependence on large amounts of labeled low-fidelity data in the training process. Two diverse temperature field prediction problems are presented to validate the effectiveness of the proposed method. The results show that our approach can significantly improve the model's accuracy, reducing the required high-fidelity data for model construction.