Knowledge-informed Bayesian optimization resolves multi-objective trade-offs in 3D printing photopolymers
The paper introduces a knowledge-informed dynamic-constrained Bayesian optimization framework (KI-DC-BO) that iteratively refines feasible search spaces based on experimental feedback to efficiently discover high-performance 3D printing photopolymers with superior tensile strength and thermal resistance within a limited experimental budget.