## 4 Discussion  
This study pioneers the first successful implementation of a deep learning-based Multilayer Perceptron (MLP) framework to achieve global-scale mapping from macroscale IGS-TEC data to the vertically layered electron density (Ne), while integrating geomagnetic (AE, SYM-H) and solar activity (F10.7) parameters. This novel framework demonstrates the transformative potential of data-driven methodologies in advancing ionospheric modeling. The discussion proceeds across three dimensions: model performance, application value, and inherent limitations. (1) Model Performance  
The experimental results demonstrate that the electron density derived from TEC decomposition achieves significant accuracy improvements over the conventional IRI-2020 model, alongside remarkably faster inference speeds. The model effectively exploits readily observable TEC data to decompose them into vertically layered Ne profiles with enhanced precision. Beyond accuracy, the model demonstrates a significant advantage in computational efficiency during inference. For example, in a test case using 10,000 data points, our deep learning model completed the inference in a few seconds, whereas the IRI-2020 model required several minutes to process the same number of points. This substantially faster inference speed makes the decomposition model highly suitable for applications requiring near real-time or real-time electron density information. (2) Application Value and Limitations  
The proposed framework demonstrates significant accuracy improvements over conventional IRI-2020 in electron density reconstruction, while achieving sub-second inference speeds. This success aligns with neural-network approaches for ionospheric tomography, particularly the Residual Minimization Training Neural Network (RMTNN) methodology that has proven effective in resolving ill-posed problems under sparse observational constraints [28]. Unlike previous regional-scale RMTNN implementations  
Figure 7: Global distribution and orbital coverage of COSMIC-2 observational data points from day of the year (DOY) 274 to 365 in 2019. Each point represents an individual observation event along the satellite trajectory, with its color indicating the magnitude of the absolute error in units of \(\ln(\mathrm{m}^{-3})\). The corresponding mean absolute error (MAE = 0.598387 \(\ln(\mathrm{m}^{-3})\)) is shown. constrained by ground-based GPS receivers [28; 29], our model innovates through two aspects: (a) global-scale generalization enabled by assimilating macroscale TEC patterns rather than localized slant TEC measurements; (b) explicit incorporation of space weather drivers (AE/SYM-H/F10.7) to capture storm-time ionospheric dynamics. The vertical resolution (~30 km) matches state-of-the-art neural tomography techniques [29] while overcoming their dependence on ionosonde-assisted altitude corrections through end-to-end TEC decomposition learning. However, the model exhibits two key limitations:
* A common limitation of purely data-driven AI models in geophysical applications, including the present study, is their limited physical interpretability. The absence of explicit ionospheric dynamical equations (e.g., continuity and momentum equations) in such frameworks restricts direct insight into underlying physical processes. * Extreme Space Weather Generalization: This study does not focus on the generalization performance of extreme space weather periods, which is worthy of further verification in the following. Future research can optimize the model performance from three aspects: (a) Integrating physics-informed approaches, such as Physics-Informed Neural Networks (PINNs) or incorporating physics-based regularization terms in the loss function (e.g., enforcing electron density vertical gradient conservation or adherence to aspects of ionospheric dynamical equations), to balance data-driven flexibility with physical consistency. This direction follows paradigms like the RMTNN approach of objective function minimization [29] but explicitly leverages physical principles.