This project built upon the EUV virtual instrument for observing in this wavelength from FDL.ai 2018; the 4pi view of the Sun from FDL.ai 2022; and the image-to-image translation approach and auto-calibration techniques of FDL.ai 2019. The goal was to ensure safer space traffic management, reduce the errors in thermospheric density modeling, and assess the impact of EUV irradiance in thermospheric density modeling.

The team was able to produce SDOML embeddings to encode the sun; use SDOML embeddings to replace solar proxies; assess the impact of SDOML embeddings as inputs for thermospheric density models; and look at differentiable physics-based models and losses for thermospheric density modeling.

This project built upon the Solar Dynamic Observator’s Machine Learning Dataset (SDOML) and the work of FDL.ai 2022 in terms of a 4pi view of the Sun across four EUV channels in order to provide 4pi datasets for EUV solar spectral irradiance. The ARD team was able to build an SDO computational platform; a cloud-based large-scale data ingestion, processing, and ML platform. They launched SDO ML V2 which includes analysis ready data for SDO AIA, HMI, and EVE from 2010 until 2023. They also created Virtual EVE, a deep learning architecture for offline and online solar irradiance forecasting based on AIA and HMI input data.

To mitigate the risk associated with geomagnetically induced currents (GICs), there is a ned to provide end-users with forecasts that are fast, localized, and actionable. This project built upon FDL.ai’s DAGGER pipeline and pursued a hybrid approach of combining ML and AI with human-in-the-loop methodologies to track the geoffective potential of solar transients.

DAGGER is now the first and only ML forecasting model able to predict localized geomagnetic fields globally; it learns the physics in multiple regimes and provides actionable forecasts through multiple lead teams and with associated uncertainty. Integration provides a basis for extended lead times, real-time data ingestion, coupling to downstream models, and operational deployment into the future.