Uncertainty-Guided Evidential Deep Learning Schemes in Full-Wave Inverse Scattering Problems

This article proposes Uncertainty-Guided Evidential Deep Learning (UGEDLS) for robust electromagnetic inverse scattering reconstruction and uncertainty estimation. We address the limitation of the standard evidential deep learning scheme (EDLS), where models tend to excessively inflate uncertainty for samples with high nonlinearity to minimize loss rather than improving prediction accuracy. UGEDLS mitigates this by penalizing unreasonable uncertainty inflation, forcing the model to focus on accurate mean reconstruction. Extensive experiments on numerical and experimental data demonstrate that UGEDLS outperforms EDLS for highly nonlinear samples in reconstruction accuracy and generalization ability, offering interpretable uncertainty estimates that effectively highlight prediction errors, particularly at scatterer boundaries.