Table . Comparison of GDL optimal scenarios for the 1000 µm x 1000 µm domain size using the deterministic learning (A) and the stochastically generated model (B). Both used the optimal manufacturing conditions as input parameters. (2) achieving a balance between all desired properties (𝐶 𝑓 4 ). The weighting scheme within the optimization framework allows for adjustments based on the targeted GDL functionality. Consequently, this approach can generate diverse optimal solutions tailored to specific industrial applications. Indeed, real-world GDL performance depends on the strategic manipulation of properties to achieve the most desirable outcomes. In this regard, the presented optimization workflow demonstrates its adaptability to various optimization problems across different application domains.