In order to understand the results of recent observations of exoplanets, models have become increasingly complex. Unfortunately, this increases both the computational cost and output size of said models. We intend to explore if AI image recognition can alleviate this burden. We used DYNAMICO to run a series of HD 209458-like models with different orbital radii. Training data for a number of features of interest was selected from the initial outputs of these models. This was used to train a pair of multi-categorization convolutional neural networks (CNNs), which we applied to our outer-atmosphere-equilibrated models. The features detected by our CNNs revealed that our models fall into two regimes: models with shorter orbital radii exhibit significant global mixing that shapes the dynamics of the entire atmosphere, whereas models with longer orbital-radii exhibit negligible mixing except at mid-pressures. Here the initial nondetection of any trained features revealed a surprise: a nightside hot spot. Analysis suggests that this occurs when rotational influence is sufficiently weak that divergent flows from the dayside to the nightside dominate over rotational-driven transport, such as the equatorial jet. We suggest that image classification may play an important role in future, computational, atmospheric studies. However special care must be paid to the data feed into the model, from the color map, to training the CNN on features with enough breadth and complexity that the CNN can learn to detect them. However, by using preliminary studies and prior models, this should be more than achievable for future exascale calculations, allowing for a significant reduction in future workloads and computational resources.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)