AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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An explainable contrastive learning model that maps 5 monthly cloud and radiation fields into a shared representation space and demonstrates that explainable representations of climate fields can attribute model differences to specific variables, regions, seasons, and physical parameters.
Training an evidential deep neural network emulator on a 500‐member CLM5 perturbed parameter ensemble generated via Latin hypercube sampling of 32 key plant physiological parameters captures seasonal cycles, interannual variability in LAI, and uncertainties in a single pass, yielding well‐calibrated probabilistic outpu...
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