2026-08-13 –, Room 4
Julia has great tools for simulating and analyzing many kinds of dynamical systems.
However, discrete, finite-state systems, i.e., systems made of many interacting parts that each have a small number of possible states, are less well supported. The class of GDS includes many well-known formalisms, such as Boolean networks, cellular automata, and sequential dynamical systems.
These models have a long history of use in the biological setting.
Boolean networks, for example, were originally introduced to model genetic regulatory networks.
More recent generalizations of Boolean networks, qualitative networks, have allowed experts to build and reason about large, complex models of signaling pathways.
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 package hooks into the JuliaDynamics,
JuliaGraphs, and soon, the AlgebraicJulia, JuliaReach, and SciML ecosystems. With this package, we hope to stimulate the implementation and development of new methods for learning and analysis of this broad class of systems.
I am a PhD candidate at TU Delft, where I focus on program synthesis and applying it to scientific discovery. Currently, I am focusing on methods for synthesizing dynamical systems, with applications in biology. Alongside my own research, I work on Herb.jl, a program synthesis framework developed here at TU Delft (see: https://herb-ai.github.io/).