# IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics

arXiv: https://arxiv.org/abs/2511.15004 · HTML: https://mlsanity.com/paper/2511.15004/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 30 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Built on by (most cited first, 3 of 3)

- [Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulations](https://mlsanity.com/paper/2601.12614/) (2026)
- [Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning](https://mlsanity.com/paper/2604.18379/) (2026)
- [Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques](https://mlsanity.com/paper/2609.31804/) (2026)

## Abstract (arXiv)

The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning models that include a GraphCast-inspired model tailored for ionospheric dynamics. IonCast leverages spatiotemporal learning to forecast global Total Electron Content (TEC), integrating diverse physical drivers and observational datasets. Validating on held-out storm-time and quiet conditions highlights improved skill compared to persistence. By unifying heterogeneous data with scalable graph-based spatiotemporal learning, IonCast demonstrates how machine learning can augment physical understanding of ionospheric variability and advance operational space weather resilience.