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In-order to understand the results of recent observations of exoplanets,models have become increasingly complex. Unfortunately this increases both thecomputational cost and output size of said models. We intend to explore ifAI-image-recognition can alleviate this burden. We used DYNAMICO to run aseries of HD209458-like models with different orbital-radii. Training data fora number of features of interest was selected from the initial outputs of thesemodels. This was used to train a pair of multi-categorisationconvolutional-neural-networks (CNN), which we applied to ourouter-atmosphere-equilibrated models. The features detected by our CNNsrevealed that our models fall into two regimes: models with a shorterorbital-radii exhibit significant global mixing which shapes the entireatmospheres dynamics. Whereas, models with longer orbital-radii exhibitnegligible mixing except at mid-pressures. Here, the initial non-detection ofany trained features revealed a surprise: a night-side hot-spot. Analysissuggests that this occurs when rotational influence is sufficiently weak thatdivergent flows from the day-side to the night-side dominate overrotational-driven transport, such as the equatorial jet. We suggest thatimage-classification may play an important role in future, computational,atmospheric studies. However special care must be paid to the data feed intothe model, from the colourmap, to training the CNN on features with enoughbreadth and complexity that the CNN can learn to detect them. However, by usingpreliminary-studies and prior-models, this should be more than achievable forfuture exascale calculations, allowing for a significant reduction in futureworkloads and computational resources.

Characterising The Atmospheric Dynamics Of HD209458b-like Hot Jupiters Using AI Driven Image Recognition/Categorisation