  Physical field reconstruction is highly desirable for the measurement and
control of engineering systems. The reconstruction of the temperature field
from limited observation plays a crucial role in thermal management for
electronic equipment. Deep learning has been employed in physical field
reconstruction, whereas the accurate estimation for the regions with large
gradients is still diffcult. To solve the problem, this work proposes a novel
deep learning method based on patchwise training to reconstruct the temperature
field of electronic equipment accurately from limited observation. Firstly, the
temperature field reconstruction (TFR) problem of the electronic equipment is
modeled mathematically and transformed as an image-to-image regression task.
Then a patchwise training and inference framework consisting of an adaptive
UNet and a shallow multilayer perceptron (MLP) is developed to establish the
mapping from the observation to the temperature field. The adaptive UNet is
utilized to reconstruct the whole temperature field while the MLP is designed
to predict the patches with large temperature gradients. Experiments employing
finite element simulation data are conducted to demonstrate the accuracy of the
proposed method. Furthermore, the generalization is evaluated by investigating
cases under different heat source layouts, different power intensities, and
different observation point locations. The maximum absolute errors of the
reconstructed temperature field are less than 1K under the patchwise training
approach.
