To mitigate the high measurement costs in electromagnetic compatibility (EMC) assessment, this paper proposes a robust active learning framework for fast 3D field reconstruction with sparse sampling. A novel “Four-Vote” query criterion is proposed to guide intelligent sample selection, which integrates Shannon entropy, committee variance, spatial density, and clustering-based representativeness, all derived from a heterogeneous radial basis function (RBF) committee. Furthermore, an adaptive polynomial degree adjustment mechanism is implemented to ensure stability in data-scarce 3D environments. Validated through full-wave HFSS simulations, the proposed method significantly outperforms traditional sampling strategies in both 2D and 3D scenarios, achieving high-fidelity field reconstruction with minimal sampling points. This framework provides an efficient solution for rapid spatial field mapping and EMC fault diagnosis in practical engineering scenarios.
Hu et al. (Mon,) studied this question.