Deep learning-based reconstruction of the structure of heterogeneous composites from their temperature fields

Abstract

Inverse problems involving transport phenomena are ubiquitous in engineering practice, but their solution is often challenging. In this work, we build a data-driven deep learning model to predict the heterogeneous distribution of circle-shaped fillers in two-dimensional thermal composites using the temperature field in the composite as an input. The deep learning model is based on convolutional neural networks with a U-shape architecture and encoding–decoding processes. The temperature field is cast into images of 128 × 128 pixels. When the true temperature at each pixel is given, the trained model can predict the distribution of fillers with an average accuracy of over 0.979. When the true temperature is only available at 0.88% of the pixels inside the composite, the model can predict the distribution of fillers with an average accuracy of 0.94, if the temperature at the unknown pixels is obtained through the Laplace interpolation. Even if the true temperature is only available at pixels on the boundary of the composite, the average prediction accuracy of the deep learning model can still reach 0.80; the prediction accuracy of the model can be improved by incorporating true temperature in regions where the model has low prediction confidence.