Past Research

past 2018

Neural Network Modeling of Ionospheric F2-Layer Electrodynamics

This project developed one of the earliest neural-network–based ionospheric models capable of predicting global F2-layer peak density (NmF2) and peak height (hmF2) from long-term satellite and ground-based measurements. The work demonstrated that machine-learning approaches can replicate large-scale ionospheric electrodynamics traditionally captured only by empirical or physics-based models.