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UID:pretalx-juliacon-2026-U9ZWXZ@pretalx.com
DTSTART;TZID=CET:20260812T172000
DTEND;TZID=CET:20260812T173000
DESCRIPTION:Hybrid climate modelling combines numerical models with machine
 -learned components. We present the development of multiple machine-learne
 d surface climate processes and their integration into the climate model S
 peedyWeather.jl using PyTorch and Lux.jl. Despite the offline training\, t
 he hybrid model is designed to generalise in space and to different climat
 es. We address speed vs. accuracy tradeoffs using SymbolicRegression.jl an
 d discuss online learning with Enzyme.jl.
DTSTAMP:20260502T104551Z
LOCATION:Room 3
SUMMARY:A learned surface roughness scheme for climate prediction in Speedy
 Weather.jl - Greg Munday\, Maximilian Gelbrecht\, Milan Klöwer\, Niklas V
 iebig
URL:https://pretalx.com/juliacon-2026/talk/U9ZWXZ/
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