Parameter Inference of Satellite Thermal Mathematical Model Using Deep Learning

Abstract

In thermal design for satellites, constructing an accurate thermal mathematical model is essential for precisely predicting the temperatures of onboard equipment. However, the current practice involves manually adjusting the parameters of the thermal mathematical model based on the results of thermal vacuum tests, which is time-consuming and costly. This study aims to enhance accuracy and efficiency by utilizing simulation-based inference (SBI) through deep learning to automate parameter adjustments. SBI using deep learning is a method that trains a deep generative model on parameters sampled from prior distributions and data generated via simulations, enabling parameter estimation that reproduces observed data without performing likelihood calculations. This approach accommodates complex and nonlinear models. Additionally, it provides posterior distributions for the estimated parameters, enabling a quantitative assessment of uncertainties. Numerical experiments using a small satellite model demonstrated the effectiveness of this approach.