## 5 Conclusions  
CFD simulations are usually computationally expensive, require high memory, and are time-consuming iterative processes. In contrast, ML and deep learning have the most efficient approach to predicting the compressor's catastrophic failure and its stability for better performance at minimum computational cost and time. Flow physics, aerodynamics, and induced forces were analyzed. Different regression model approaches in ML and deep learning were used as prediction analysis for multiple independent variables simultaneously. The following conclusions can be drawn from the current work:  
* Hub radial pressure distortion has improved the stability range of the compressor, whereas tip radial inlet flow distortion has deteriorated the compressor's performance.
* Detailed analysis of combined distortions in CFD analysis suggests that adding new distortion, i.e., either co-swirl flow or counter swirl flow in already existing inlet distortion, has a qualitative effect on the compressor. As distortion has a deteriorating effect on compressor performance, we cannot predict its impact on compressor stability.
* Different regression models in ML were used for prediction analysis of the compressor rotor performance. The results obtained from the different regression models depict a promising approach to predicting the compressor variables with minimum error. In contrast, the GPR algorithm was able to learn and trained the CFD-based dataset, thus providing promising results for prediction analysis.
* The results obtained from the Deep learning Artificial Neural Network (ANN) show that, despite using a conventional method of predicting parameters through CFD analysis, the use of ANNs is a promising approach to predicting the compressor's parameter rotor blade.
* The resulting regression learner and Artificial Neural Networks have a less than 1% prediction error at a computational cost, which is several times lesser than the underlying CFD solver's cost.  
**Author Contributions:** Conceptualization, H.R.H. and K.P.; Formal analysis, M.U.S., H.R.H.; Investigation, M.U.S. and K.P.; Methodology, M.U.S., H.R.H.; Resources, A.M.K.; Software, M.U.S.; Supervision, H.R.H., K.P., A.M.K., Validation, M.U.S., K.P.; Visualization, H.E., M.K.; Writing original draft, M.U.S., U.A.; Writing review & editing, M.U.S., M.K., and A.I.; Result Discussion--M.U.S., A.I. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** I would like to thank [PERSON] from Tsinghua University, China, and [PERSON] from UNIMAS, Malaysia expert reviews, valuable suggestions for the completion of the research. Furthermore, I also thank the Institute of Space Technology, Islamabad, for providing me computational resources and labs.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
Adam Adaptive Moment Estimation  
ANN Artificial Neural Network  
CFD Computational fluid dynamics  
CNN Convolutional neural network  
DL Deep Learning  
ISA International Standard Atmosphere  
KNN k-Nearest Neighbors  
ML Machine Learning  
MIV Multiple Independent Variables  
MSE Mean Square Error  
ReLU Rectified Linear Unit  
RFM Revolution per minute  
SVM Support Vector Machine  
GPR Gaussian Process Regression  
SIV Single Independent Variables  
TC Tip clearance  
\(\mu\) Sutherland viscosity  
\(\alpha\) Flow Angle  
Po Total Pressure