论文标题
物理知识的深蒙特卡洛卡洛分位回归方法用于间隔多级贝叶斯网络的卫星热可靠性分析
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
论文作者
论文摘要
温度场重建对于分析卫星热可靠性至关重要。作为代表性的机器学习模型，深度卷积神经网络（DCNN）是重建卫星温度场的强大工具。但是，DCNN需要大量标记的数据来学习其参数，这与实际卫星工程只能获取嘈杂的未标记数据相反。为了解决上述问题，本文提出了一种无监督的方法，即物理学深的蒙特卡洛分位数回归方法，用于重建温度场并量化由数据噪声引起的不确定性。一方面，提出的方法将深层卷积神经网络与已知的物理知识结合在一起，以仅使用监测点温度来重建准确的温度场。对于另一件事，提出的方法可以通过蒙特卡洛分位回归来量化存在的不确定性。基于重建的温度场和量化的息肉不确定性，本文对一个间隔多级贝叶斯网络进行了建模，以分析卫星热可靠性。两个案例研究用于验证所提出的方法。
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.
