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SUMMARY:Optimal Uncertainty Quantification of SciML Models - Chris Rackauc
 kas\, Adam R. Gerlach\, Avinash Subramanian\, Benjamin Chung\, Alexander V
 on Moll
DTSTART;TZID=America/New_York:20250723T150000
DTEND;TZID=America/New_York:20250723T151000
DTSTAMP:20260812T143006Z
UID:pretalx-juliacon-2025-EJSMQD@pretalx.com
DESCRIPTION:We present OptimalUncertaintyQuantification.jl: A SciML packag
 e for end-to-end distributionally robust uncertainty quantification of sta
 tic and dynamic systems models. The tool performs a worst-case analysis so
  as to make certification/decertification decisions on engineering models 
 defined in ModelingToolkit.jl as demonstrated on a variety of aerospace an
 d structural engineering applications.
LOCATION:Cathedral Room 324 - Else Room
URL:https://pretalx.com/juliacon-2025/talk/EJSMQD/
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