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Abstract

The climate and weather are modeled by running computer simulations. In a data-driven approach, scientists tailor the simulation to resemble reality (partly through an understanding of the physical processes, partly through their parameterization). With the availability of large data and compute, we can hope for machines to help in the automatic discovery of principles from data. The integration of machine learning (deep learning) within simulations opens up the exploration of new tradeoffs between accuracy and computational cost.

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