A Novel Deep Learning Approach for TEC Map Completion Using Image Equation‐Guided Loss

Abstract This study introduces the Image Equation Mechanism‐Completion Generative Adversarial Network (IEM‐CGAN), a groundbreaking deep learning framework that revolutionizes the reconstruction of large‐scale global ionospheric total electron content (TEC) maps. The model achieves unprecedented accuracy and robustness in filling extensive data gaps by innovatively embedding image equation mechanisms, including biharmonic, Allen‐Cahn, and reaction‐diffusion equations—as novel loss functions into the adversarial training process. Leveraging the IGS‐TEC and MIT‐TEC data sets, the framework demonstrates exceptional performance, achieving SSIM scores of 92.25%–95.40%, correlation coefficient (CC) of 97.61%–98.30%, and reducing RMSE to a mere 0.93–3.43 TECU. Remarkably, the IEM‐CGAN significantly outperforms conventional CGAN models, reducing RMSE errors by 29%–55% and improving SSIM by up to 7.49%, setting a new benchmark for ionospheric data completion. These advancements not only address critical challenges posed by uneven ground‐station coverage but also provide a transformative tool for space weather forecasting, satellite navigation, and geophysical imaging, with