BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//juliacon-2026//talk//SFWUKP
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-juliacon-2026-SFWUKP@pretalx.com
DTSTART;TZID=CET:20260814T100000
DTEND;TZID=CET:20260814T103000
DESCRIPTION:Real-time adaptive experimental design for ODE models is hard: 
 each step requires costly posterior inference and optimization. We train a
  neural network policy offline to amortize this cost. The Julia SciML stac
 k makes this practical: Enzyme.jl differentiates through ODEs\, Lux.jl def
 ines the policy network\, and Reactant.jl compiles everything to a single 
 GPU program. On a bioreactor benchmark\, the learned adaptive policy beats
  Bayesian D-optimal static designs with a 99.5% win rate.
DTSTAMP:20260502T104021Z
LOCATION:Room 1
SUMMARY:Deep Adaptive Experimental Design for SciML - Arno Strouwen\, Sebas
 tian Micluța-Câmpeanu
URL:https://pretalx.com/juliacon-2026/talk/SFWUKP/
END:VEVENT
END:VCALENDAR