AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This evaluation framework provides a reproducible benchmark for quantifying parametric climate knowledge in LLMs, and shows that LLMs reproduce broad geographic regularities in climatological temperature fields, capturing latitudinal and topographic patterns.
HlimRep-Ocean is presented, an ocean emulator that operates directly on the native unstructured mesh of FESOM2, and achieves the lowest RMSE against GLORYS reanalysis among all assessed systems, confirming the competitiveness of the native-mesh approach.
Abstract. The increasing availability of kilometer-scale climate simulations presents major challenges for data access, processing, and analysis due to the unprecedented volume and heterogeneity of the outputs. Different data formats, structures, and metadata conventions, require dedicated solutions to ensure interoper...
We present FESOM2-JAX, a Python re-implementation of the Finite-volumE Sea ice-Ocean Model (FESOM2) in JAX. The model retains the unstructured-mesh, cell-vertex finite-volume formulation of the original, runs unchanged from a laptop CPU to 256 GPUs, and is end-to-end differentiable. FESOM2-JAX is a code shadow of the F...
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