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UID:pretalx-juliacon-2026-A9PDA7@pretalx.com
DTSTART;TZID=CET:20260812T120000
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DESCRIPTION:Hyperparameter optimization is a core workflow in machine learn
 ing and scientific computing\, yet the Julia ecosystem has lacked a mature
 \, production-ready framework comparable to the robust\, battle-tested too
 ls available in other languages. In order to bridge this gap\, we present 
 `Optuna.jl` a package that brings the full functionality of `Optuna` (by P
 referred Networks\, Inc.)\, one of the most widely adopted hyperparameter 
 optimization frameworks\, into Julia.
DTSTAMP:20260502T104012Z
LOCATION:Room 2
SUMMARY:Optuna.jl - Hyperparameter optimization with Optuna in Julia - Juli
 an Trommer\, Lars Mikelsons
URL:https://pretalx.com/juliacon-2026/talk/A9PDA7/
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UID:pretalx-juliacon-2026-KKHD78@pretalx.com
DTSTART;TZID=CET:20260814T113000
DTEND;TZID=CET:20260814T114500
DESCRIPTION:Functional Mock-up Units (FMUs) are widely used in industry for
  exchanging dynamical system models\, but their black-box binary nature ma
 kes them inaccessible to traditional AD tools. Built-in derivative support
  in the FMI standard is limited in scope and often relies on slow finite d
 ifferences. We present a novel approach: by embedding LLVM bitcode into FM
 U binaries during compilation\, we make them accessible to Enzyme.jl\, ena
 bling fast\, automatic differentiation of virtually any FMU function.
DTSTAMP:20260502T104012Z
LOCATION:Room 6
SUMMARY:Differentiating Functional Mock-up Units (FMUs) with Enzyme: Fast A
 D for Black-Box Simulation Models - Valentin Höpfner\, Lars Mikelsons
URL:https://pretalx.com/juliacon-2026/talk/KKHD78/
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