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Forecasting and Modelling Space Weather with Deep Learning Methods https://doi.org/10.17863/CAM.117523 Brown, Edward 0000000247199518 Lane, Nicholas With the ever increasing number of spacecraft providing essential services for society, the accurate modeling and forecasting of conditions for these spacecraft becomes increasingly important. The conditions that these spacecraft operate in is often referred to as space weather. The advent of deep learning has unlocked the ability to use large datasets to model and forecast these conditions. This thesis principally describes a set of methodological improvements, considerations and proof of concept systems that use extreme ultra-violet (EUV) solar images and deep learning techniques to forecast and model space weather conditions. Firstly, vision transformers are used to forecast solar wind speed from solar EUV images, with improvements over previous work. Secondly, solar irradiance is forecast using pre-trained vision transformers that consume nine solar EUV/UV image channels, with its performance explored. Thirdly, autoencoders are trained to create new solar indices that can be used to forecast various space weather phenomena to significant effect, motivating the use of such indices in production systems. Lastly, thermospheric density models are trained that can significantly outperform existing physics-based models. University of Cambridge 2024-02-25 Thesis Doctoral Doctor of Philosophy (PhD) eng https://www.repository.cam.ac.uk/handle/1810/382941 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/4aa75411-5510-4dd4-8033-9a0a8abe561e/download 931c987f5021cf476b3ede170accd07f https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/2db5e448-1d82-423d-aac6-fb8f74309227/download 87eda9de84448d1f82354d60eee3eb5f https://creativecommons.org/licenses/by/4.0/ deep learning heliophysics machine learning space weather thermosphere