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UID:pretalx-juliacon-2026-9MGLXG@pretalx.com
DTSTART;TZID=CET:20260813T123000
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DESCRIPTION:Julia has great tools for simulating and analyzing many kinds o
 f dynamical systems.\nHowever\, discrete\, finite-state systems\, i.e.\, s
 ystems made of many interacting parts that each have a small number of pos
 sible states\, are less well supported.  The class of GDS includes many we
 ll-known formalisms\, such as Boolean networks\, cellular automata\, and s
 equential dynamical systems. \nThese models have a long history of use in 
 the biological setting.\nBoolean networks\, for example\, were originally 
 introduced to model genetic regulatory networks.\nMore recent generalizati
 ons of Boolean networks\, _qualitative_ networks\, have allowed experts to
  build and reason about large\, complex models of signaling pathways.\n\n\
 n`GraphDynamicalSystems.jl` provides a common backbone for constructing\, 
 learning\, executing\, and analyzing GDS. As these models are 1) dynamical
  systems\, 2) graphs\, and 3) compositional in their behavior\, they make 
 for a great use case for recombining different packages and ecosystems to 
 create something new—something Julia excels at. In this case\, the packa
 ge hooks into the `JuliaDynamics`\, \n`JuliaGraphs`\, and soon\, the `Alge
 braicJulia`\, `JuliaReach`\, and `SciML` ecosystems. With this package\, w
 e hope to stimulate the implementation and development of new methods for 
 learning and analysis of this broad class of systems.
DTSTAMP:20260502T104507Z
LOCATION:Room 4
SUMMARY:GraphDynamicalSystems.jl: discrete\, finite-state systems over grap
 hs - Reuben Gardos Reid
URL:https://pretalx.com/juliacon-2026/talk/9MGLXG/
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