A deep learning model with dynamic masks and multi-parameter constraints predicts solar vector magnetic fields over 12 hours, achieving SSIM 0.912 and CC 0.998 for the radial component with 7.82% unsigned flux error.
Sta- tistical analyses of solar active region in SDO/HMI magnetograms detected by unsuper- vised machine learning method DSARD
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Deep Learning with Magnetic Parameter Constraints for Short-Term Prediction of Solar Active Region Vector Magnetic Fields
A deep learning model with dynamic masks and multi-parameter constraints predicts solar vector magnetic fields over 12 hours, achieving SSIM 0.912 and CC 0.998 for the radial component with 7.82% unsigned flux error.