Deep Learning with Magnetic Parameter Constraints for Short-Term Prediction of Solar Active Region Vector Magnetic Fields

Forecasting the dynamic evolution of solar magnetic fields is a critical technique for enabling space-weather warnings. Addressing the limitations of existing methods in predicting all vector magnetic field components and in maintaining consistency with solar surface magnetic-field-related quantities, this study proposes a deep learning prediction method that integrates dynamic masks of active regions with multiple magnetic parameter constraints. By constructing a three-channel representation of vector magnetic fields, applying dynamic masks to enhance attention to strong-field regions, and incorporating multi-parameter magnetic parameter constraints, we developed an end-to-end short-term (12-hour) predictive model of solar vector magnetic field evolution. Using SDO/SHARP vector magnetogram data, the model predicts and analyses field evolution across all components. Quantitative evaluations demonstrate that our approach achieves horizon-averaged structural similarity index measure (SSIM) of 0.912 (per-hour range: 0.909 – 0.916) and correlation coefficient (CC) of 0.998 for the radial component $B_r$ $B_{r}$ (root-mean-square error (RMSE) 13.0 – 21.0 G); the horizontal components achieve $B_{\varphi }$ $B_{\phi }$ SSIM 0.760 – 0.800 (CC 0.910 – 0.945, RMSE 38.5 – 50.0 G) and $B_{\theta }$ $B_{\theta }$ SSIM 0.728 – 0.750 (CC 0.895 – 0.920, RMSE 38.5 – 49.0 G). The model maintains unsigned magnetic-flux prediction errors at 7.82% (95% confidence interval (CI): ±0.11%). These results demonstrate strong image-domain performance together with consistency under the magnetic-parameter diagnostics used here, suggesting initial potential for supporting future space weather forecasting efforts.