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UID:pretalx-juliacon-2026-3RDRVU@pretalx.com
DTSTART;TZID=CET:20260814T114500
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DESCRIPTION:Ocean models simulate complex physics\, but struggle from inher
 ent limitations and under-resolved phenomena. This motivates the use of in
 verse and machine learning methods to inform models with data. We have imp
 lemented automatic differentiation in the Ocean modeling package Oceananig
 ans.jl\, through the use and enhancement of compiler tools Enzyme.jl and R
 eactant.jl. Using these open-source packages\, we generate gradients for a
 pplications like parameter estimation and embedded ML techniques.
DTSTAMP:20260710T083413Z
LOCATION:Alte Mensa — Atrium Maximum
SUMMARY:DJ4Oceananigans: Differentiating an ocean general circulation model
  for gradient-based parameter calibration and online learning - Joseph Kum
 p
URL:https://pretalx.com/juliacon-2026/talk/3RDRVU/
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