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UID:pretalx-juliacon-2026-EYUQVV@pretalx.com
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DESCRIPTION:Automatic Differentiation (AD) methods present an efficient way
  for computing function derivatives\, however the sheer amount of the impl
 emented methods can be overwhelming. \nChosing the correct method for the 
 task can have crucial impact on the performance[1]\, hence understanding t
 he differences between them and their use cases is beneficial.
DTSTAMP:20260529T230839Z
LOCATION:Room 6
SUMMARY:Different Automatic Differentiation algorithms from `SciMLSensitivi
 ty.jl`\, and when to use them. - Marko Polic
URL:https://pretalx.com/juliacon-2026/talk/EYUQVV/
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