Fig. 7: Schematic representation of an experimental campaign utilizing the proposed framework.
From: Bayesian optimization with active learning of design constraints using an entropy-based approach
The process begins with a closed-loop exploration of the design space to identify the range of compositions that meet all requirements. This forms the feasible region of the design space. Optimization is then carried out within this region to identify a set of Pareto-optimal alloys as the final outcome of the campaign.
