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SUMMARY:StochasticAD.jl: Differentiating discrete randomness - Frank Schä
 fer\, Gaurav Arya
DTSTART;TZID=US/Eastern:20230726T163000
DTEND;TZID=US/Eastern:20230726T170000
DTSTAMP:20260816T101118Z
UID:pretalx-juliacon2023-RRBDAA@pretalx.com
DESCRIPTION:Automatic differentiation (AD) is great: use gradients to opti
 mize\, sample faster\, or just for fun! But what about coin flips? Agent-b
 ased models? Nope\, these aren’t differentiable... or are they? Stochast
 icAD.jl is an open-source research package for AD of stochastic programs\,
  implementing AD algorithms for handling programs that can contain discret
 e randomness.
LOCATION:32-D463 (Star)
URL:https://pretalx.com/juliacon2023/talk/RRBDAA/
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SUMMARY:Differentiation of discontinuities in ODEs arising from dosing - F
 rank Schäfer
DTSTART;TZID=US/Eastern:20230728T114000
DTEND;TZID=US/Eastern:20230728T115000
DTSTAMP:20260816T101118Z
UID:pretalx-juliacon2023-YRTLEC@pretalx.com
DESCRIPTION:In this talk\, we present continuous-adjoint sensitivity metho
 ds for hybrid differential equations (i.e.\, ordinary or stochastic differ
 ential equations with callbacks) modeling explicit and implicit events. Th
 e methods are implemented in the SciMLSensitivity.jl package. As a concret
 e example\, we consider the sensitivity analysis of dosing times in pharma
 cokinetic models. We discuss different options for the automatic different
 iation backend.
LOCATION:32-D463 (Star)
URL:https://pretalx.com/juliacon2023/talk/YRTLEC/
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SUMMARY:Convex Optimization for Quantum Control in Julia - Flemming Holtor
 f\, Frank Schäfer
DTSTART;TZID=US/Eastern:20230728T160000
DTEND;TZID=US/Eastern:20230728T161000
DTSTAMP:20260816T101118Z
UID:pretalx-juliacon2023-MX7J3F@pretalx.com
DESCRIPTION:Feedback control policies for quantum systems often lack perfo
 rmance targets and certificates of optimality. Here\, we will show how bou
 nds on the best possible control performance are readily computable for a 
 wide range of quantum control problems by means of convex optimization usi
 ng Julia's optimization ecosystem. We discuss how these bounds provide tar
 gets and certificates to improve the design of quantum feedback controller
 s.
LOCATION:26-100
URL:https://pretalx.com/juliacon2023/talk/MX7J3F/
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