In today’s competitive aerospace industry, powerful computational tools are becoming increasingly accessible, thanks to significant advancements in GPU technology and the parallel processing capabilities provided by CUDA software. As a result, high-fidelity simulations are playing a growing role in industrial aerodynamic design, offering highly reliable solutions. However, the large volume of data generated by these simulations presents a pressing need for data reduction techniques and advanced post-processing tools that can keep pace with these developments, enabling comprehensive analysis of complex non-linear dynamical systems. This thesis aims to develop advanced data-driven post-processing techniques for high-fidelity simulations, specifically to improve the analysis of low-pressure turbine cascade performance.