HAL (France)
open access
Interpreting atmospheric neural networks: insights from explainable AI
We demonstrate how explainable artificial intelligence (XAI) methods can increase confidence in neural network predictions for exoplanet atmospheric modelling. We analysed a convolutional U-Net trained to reconstruct disequilibrium steady states from equilibrium chemistry states. We evaluated how th
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