Recently, more flexible and adaptive data-driven approaches, such as Bayesian optimization (BO) methods , have been proposed as a solution, making it possible to adjust the experimental planning dynamically based on accumulated information. For instance, different surrogate-based optimization methods have been developed over the years. By incorporating Bayesian principles, the experimental space can be navigated more effectively, allowing for more comprehensive exploration and a better chance of uncovering hidden insights that might be overlooked by conventional DoE approaches. This emphasizes the importance of considering alternative approaches, particularly those with enhanced exploration capabilities, to ensure a more thorough and insightful understanding of the underlying phenomena. Prior works have extensively explored surrogate modeling techniques such as Gaussian processes , radial basis functions , and neural networks to approximate the underlying correlations between the design variables and the desired outcomes.