## 1 Introduction
The current state-of-the-art work is on molecular dynamics simulation of interlayer gases for MLI. [PERSON] [13] used the Test Particle Monte Carlo (TPMC) method to investigate the outgassing behavior of MLI setups at the end of pumping. Theoretically, simulations with TPMC methods can provide pressure values at arbitrary locations inside the MLI and even the pressure distribution through the thickness of the MLI [13]. In their study, a set of symmetric, separated, perforated, square-shaped layers were investigated. To reduce the computational time, two symmetry planes were modelled as reflective walls and the other three-quarters of the setup were discarded. However, for a practical large cryogen (e.g., liquid hydrogen) container, the MLIs may consist of hundreds of layers and the area of MLIs may be hundreds of square meters. At \begin{table}
\begin{tabular}{c c c c} \hline
**Calorimeter Type** & **Height/mm** & **Diameter/mm** & **Vacuum Chamber Pressure** \\ \hline Cylindrical [5] & 1270 & 230 & \(<\)6.7 \(\times\) 10\({}^{-3}\) Pa \\ Cylindrical [6] & 300 & 200 & 1 \(\times\) 10\({}^{-5}\) Pa \\ Cylindrical [7; 8] & 1000 & 167 & \(<\)6.7 \(\times\) 10\({}^{-3}\) Pa \\ Cylindrical [9; 10] & 3050 & 3050 & \(<\)1.33 \(\times\) 10\({}^{-4}\) Pa \\ Elliptical [11] & 1.42 m\({}^{3}\) & \(\sim\) \\ Spherical and cylindrical [12] & 0.055 m\({}^{3}\) & \(6\times\) 10\({}^{-4}\) Pa \\ \hline \end{tabular}
\end{table} 
Table 1: Test sets of MLI studies with small calorimeters. present, it seems to be impossible to reveal the residual pressure distribution in MLIs for a practical engineering design. The cause of this problem, which is inaccurate and extremely difficult to solve in the forward direction, can be inferred by inversion, and the methods used generally rely on machine learning techniques to extract mapping relationships between implicit variables based on large amounts of data, which have wide applications. [PERSON] (2014) used a kernel-based stereological model for the inversion of particles produced from the crushing. [PERSON] (2015) used statistical machine learning techniques from the Mercer kernel-based support vector machine applied to the inversion of the aperture magnetic field. Based on such an approach, the idea is to improve the prediction accuracy of insulation performance by using easily measurable interlayer temperatures, relying on a modified LBL model, and inferring the distribution of interlayer pressures. [PERSON] (2016) compared the accuracy of different machine learning models on pumpkin seed classification, e.g., Logistic Regression, Support Vector Machine, Decision Trees, Naive Bayes and k-nearest neighbor classification, precision and recall on a sample of 2500 obtained from Kaggle. The decision tree model was found to perform better than the other models. [PERSON] (2017) proposed an incremental extreme learning machine model (IELM) to improve the robot localization accuracy. An extreme learning machine is optimized by an improved sparrow search algorithm (ISSA) and is used to predict the localization error of industrial robots. The predicted errors are used to achieve compensation for target points in the robot workspace. The IELM model has good fitting and prediction capabilities and can be fine-tuned by adding fewer samples. Compared with other neural network regression methods, the structure of the extreme learning machine is very simple, and the output weights are obtained by the least squares method. This makes the training quick and suitable for offline data prediction. Therefore, ELM has good prospects for industrial applications. In this study, an extreme learning machine (ELM) neural-network-based inversion model is proposed to reconstruct the interlayer pressure distribution of high-vacuum multilayer insulation for liquid hydrogen containers. The dataset used in this paper is manually designed to contain U- and L-shaped distributions that are demonstrated in the available literature in multilayer insulation interlayer pressure studies.