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UID:pretalx-juliacon-2026-GB8WXW@pretalx.com
DTSTART;TZID=CET:20260812T152000
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DESCRIPTION:Traditionally\, climate models are difficult to run for end use
 rs\, and even harder to customize or interface with machine learning. We w
 ant to change that. Here\, we present the ongoing development of SpeedyWea
 ther.jl: A general circulation model that’s differentiable\, GPU-capable
  and ready for machine learning integration. SpeedyWeather.jl is a spectra
 l atmospheric general circulation model with an everything-flexible attitu
 de. In this talk\, we will give an overview of SpeedyWeather.jl’s develo
 pment of the last year\, in which we worked towards differentiability with
  Enzyme\, GPU-capability with KernelAbstractions and Reactant and rewrote 
 our parametrizations for better performance and more customisability.
DTSTAMP:20260502T104550Z
LOCATION:Room 3
SUMMARY:SpeedyWeather.jl: Towards a differentiable and GPU-capable general 
 circulation model - Maximilian Gelbrecht\, Milan Klöwer
URL:https://pretalx.com/juliacon-2026/talk/GB8WXW/
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