However, challenges remain in handling large-scale datasets efficiently. Future research should explore alternative low-rank approximation techniques beyond SVD to further optimize computational speed and accuracy . Additionally, investigating advanced sampling approaches that enable direct sampling from high-dimensional spaces could potentially accelerate the analysis process . Finally, the integration of scalable GP approximations such as sparse GPs and distributed GPs, could improve scalability and performance for large-scale or distributed EIS data analysis . These advancements could collectively lead to faster analysis time, greater accuracy, and the ability to handle larger EIS datasets more effectively.