Physics-informed time-dependent deep neural network for solar wind prediction

Veronique Delouille; Kaijie Li; Farzad Kamalabadi; Joseph Davila

doi:https://doi.org/10.5194/egusphere-egu26-22082

[Back] [Session ESSI1.18]

EGU26-22082, updated on 14 Mar 2026

https://doi.org/10.5194/egusphere-egu26-22082

EGU General Assembly 2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Physics-informed time-dependent deep neural network for solar wind prediction

Veronique Delouille¹, Kaijie Li², Farzad Kamalabadi², and Joseph Davila³

Veronique Delouille et al.

¹Royal Observatory of Belgium, Brussels, Belgium
²University of Illinois Urbana-Champaign, Urbana, USA
³Retired, formerly at NASA GSFC, Washington DC, USA

How to cite: Delouille, V., Li, K., Kamalabadi, F., and Davila, J.: Physics-informed time-dependent deep neural network for solar wind prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22082, https://doi.org/10.5194/egusphere-egu26-22082, 2026.

This abstract has been withdrawn after no-show on 11 Aug 2026.