## 1 Introduction
[3] investigated a predictive approach based on an Artificial Neural Network (ANN) to predict the transonic compressor performance and behavior at icy, moderate, and extreme hot diversified ambient temperature conditions due to seasonal effects under design RPM. Their model produces substantially accurate results of a compressor rotor at different ambient temperatures when compared with the results of CFD analysis. The results visualized through unity plots are a clear indication that, given any set of temperature and pressure values, the trained model can accurately generate predictions of mass flow rate, temperature ratio, pressure ratio, and efficiency in less computational time, as compared with simulating the models through CFD analysis. However, variable RPM, pressure distortion, bulk flows, and their combined effects on the compressor were not investigated. Furthermore, a shallow Artificial Neural Network model had been developed and trained concerning the same set of features. [PERSON] et al. applied reduced-order modeling technology to construct reduced-order models (ROMs) based on the multi-fluid model from CFD data, to simulate biomass rapid pyrolysis in a bubbling fluid bed reactor. CFD calculations were conducted at nine different pyrolysis temperatures. Artificial Neural Network back-propagation was used to map the species mass fraction data of the CFD simulation onto the pyrolysis temperature and the coordinates of each computational node in the reactor. The number of neurons and the active function of the ANN had been optimized. The ability of the established ROMs to predict species distribution at both training and testing temperatures was investigated [4]. [PERSON] et al. [5] investigated the centrifugal compressor performance maps of pressure ratio and isentropic efficiency from a shallow dataset using an ANN approach. They developed kriging models based on second-order polynomial and Neural Network models to predict centrifugal compressor performance maps with the limited dataset. [PERSON] et al. [6] presented an ML regression model to predict the high-pressure hydrogen compressor material and develop a new alloy combination with favorable enthalpies by using an existing open-source database. Their results concluded 28% of mean relative error, whereas due to techno-economic analysis and material cost, composition constrained the new hybrid materials for experimental verification. [PERSON] et al. [7] analyzed the new vehicle compressor faults prediction by on-board Volvo truck logged data using ML analysis. Although their results showed that predictive maintenance was possible using the ML approach, due to shallow dataset classification, quality, and cost avoidance, it did not get accurately predicted. The contribution of their ML predictive fault-finding work is highly regarded for identifying the distinctive features of the automobile industry. In the past, curve fitting, auxiliary coordinates, and non-linear function analytical functions are used for the prediction of the compressor performance map. However, for the mapping approach, scaling and shifting technology have proved more fidelity and accuracy of the gas turbine compressor. [PERSON] et al. [8] proposed Gaussian kernel, feed-forward back-propagation of ANN for prediction of compressor performance map at the ideal conditions with a minimal data sample. Prediction accuracy decreases as the number of samples reduces. Different models were compared in detail with the limited dataset, and it was concluded that the Gaussian kernel function Backpropagation Neural Network (GBPNN) had better prediction performance than the Backpropagation Neural Network (BPNN) and support vector machine (SVM). Recently, NASA researcher [PERSON] [9] explored the applications of KNN, ANN, and SVM supervised ML approaches to predict the engine core size. For the turbofan engine, machine learning-based predictive tools were developed using the publicly available data of two hundred manufactured engines.