Physics‐Embedded Deep Learning Reveals the Memory Effect of Ionospheric Electron Density in Response to Solar EUV Forcing

Abstract Precise characterization of the electron density profile is crucial for optimizing high frequency communications. Reconstruction models often assume an instantaneous ionospheric response to solar forcing or using time delays, neglecting the time‐integrated cumulative “memory effect” of plasma dynamics. Here, we develop the Electron Density Profile Reconstruction Neural Network (EDPRNN), a physics‐embedded framework that parameterizes and dynamically learns this memory representation from data. Using observations from three mid‐ and low‐latitude digisondes, we reveal that the learned memory exhibits distinct altitude and latitude dependence. Below the F1 layer, this data‐driven memory strongly correlates ( r ≈ 0.97) with theoretical photochemical time constants at all stations. EDPRNN also performs well on an independent solar maximum test set, with coefficient of determination ( R 2 ) values of 0.939, 0.905, and 0.841 at Mohe, Zuoling, and Fuke, respectively. These results indicate that EDPRNN provides an effective ionospheric nowcasting method and a novel data‐driven framework for identifying a physically consistent memory effect.