ABSTRACT 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.
Zhang et al. (Wed,) studied this question.