Parameter Inference of Spacecraft Thermal Mathematical Model Using Deep Learning

Abstract

In spacecraft thermal design, constructing an accurate thermal mathematical model is crucial for reliably predicting the temperatures of various components. However, current practices involve manually adjusting model parameters based on thermal vacuum test results, which is both time-consuming and costly. This study proposes the use of simulation-based inference (SBI) for estimating the parameters. SBI offers the flexibility to handle complex and nonlinear models. Additionally, it provides posterior distributions for the estimated parameters, enabling a quantitative assessment of uncertainties and confidence intervals. To validate the effectiveness of this approach, numerical experiments are conducted using a small satellite model under simplified conditions.