Structural Optimization of 3D Braided Tungsten–Copper Composites via Active Learning and Finite Element Simulation

Three-dimensional (3D) braided tungsten–copper (W–Cu) composites are attractive for combining high strength with good electrical performance, yet the high-dimensional design space makes manual optimization impractical. This work integrates high-fidelity finite element modeling (FEM) with an active learning (AL) scheme driven by a Gaussian process regression (GPR) surrogate and an uncertainty-aware acquisition policy. The surrogate provides accurate predictions together with quantified uncertainty, enabling data-efficient exploration of candidate braiding parameters while satisfying a prescribed electrical conductivity requirement. The framework rapidly identifies an optimal braided architecture with a peak yield strength of 1149 MPa, approximately a 30% improvement over the best design in the initial set. SHAP-based interpretability further reveals physically meaningful guidelines, with the in-plane spacing coefficient of the Z-direction fibers emerging as the dominant factor. These results demonstrate a fast interpretable route for optimizing metallic braided architectures and highlight the broader utility of FEM-coupled active learning for materials design.