Fig. 2: Overall results of the 5-constraint 3-objective material design problem.
From: Bayesian optimization with active learning of design constraints using an entropy-based approach
The figure shows the application of the proposed framework to solve the problem. The process begins with learning the constraint boundaries by querying the constraints, effectively reducing the entropy associated with each classifier that represents a specific constraint. Once the entropy curves for all classifiers are flattened, Bayesian optimization begins to learn the non-dominated design region. As the estimations of the Pareto front improve, the hypervolume increases respectively. The figure also includes an illustration of the objective space, showing all the queries to the ground truth model and the final estimation of the Pareto front.
