  Temperature field reconstruction is essential for analyzing satellite heat
reliability. As a representative machine learning model, the deep convolutional
neural network (DCNN) is a powerful tool for reconstructing the satellite
temperature field. However, DCNN needs a lot of labeled data to learn its
parameters, which is contrary to the fact that actual satellite engineering can
only acquire noisy unlabeled data. To solve the above problem, this paper
proposes an unsupervised method, i.e., the physics-informed deep Monte Carlo
quantile regression method, for reconstructing temperature field and
quantifying the aleatoric uncertainty caused by data noise. For one thing, the
proposed method combines a deep convolutional neural network with the known
physics knowledge to reconstruct an accurate temperature field using only
monitoring point temperatures. For another thing, the proposed method can
quantify the aleatoric uncertainty by the Monte Carlo quantile regression.
Based on the reconstructed temperature field and the quantified aleatoric
uncertainty, this paper models an interval multilevel Bayesian Network to
analyze satellite heat reliability. Two case studies are used to validate the
proposed method.
