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UID:pretalx-juliacon-2026-DTEEQC@pretalx.com
DTSTART;TZID=CET:20260812T170000
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DESCRIPTION:Scientific modeling often forces a choice between flexible but 
 opaque neural networks and interpretable process-based models that can be 
 too rigid for real-world data. Hybrid modeling bridges this gap by combini
 ng mechanistic structure with machine-learning flexibility. In this talk w
 e introduce EasyHybrid.jl\, a user-friendly Julia package that makes hybri
 d modeling accessible across disciplines. We demonstrate the approach on a
  concrete problem: partitioning eddy-covariance net carbon fluxes into pho
 tosynthesis (a CO₂ sink) and ecosystem respiration (a CO₂ source)\, wh
 ile estimating how strongly respiration responds to temperature. Temperatu
 re sensitivity is summarized by Q10\, the factor by which respiration chan
 ges for a 10 K warming (e.g.\, Q10 = 2 means doubles per 10 K). We present
  cross-site results across hundreds of FLUXNET eddy covariance towers and 
 show that\, even when inferred jointly with hybrid flux partitioning\, the
  learned temperature sensitivity exhibits a relatively narrow convergence 
 across ecosystems.
DTSTAMP:20260428T134301Z
LOCATION:Room 3
SUMMARY:Hybrid Flux Partitioning in Julia: Learning Temperature Sensitivity
  of Ecosystem Respiration with EasyHybrid.jl - Bernhard Ahrens\, RITESH MO
 ON\, Lazaro Alonso
URL:https://pretalx.com/juliacon-2026/talk/DTEEQC/
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