JuliaCon 2020 | NetworkDynamics.jl - Modeling dynamical systems on networks | Michael Lindner

NetworkDynamics.jl is a tool for dynamical modeling and analysis of large, inhomogeneous, networked systems. It provides a convenient interface between LightGraphs.jl and DifferentialEquations.jl.

We introduce the basic syntax of our package and showcase applications ranging from neurodynamics to power systems. We conclude with a brief overview of advanced features such as multi-threading, support for SDEs and integration with the machine learning environment DiffEqFlux.jl.

NetworkDynamics.jl is developed at Potsdam Insitut for Climate Impact Research (PIK) to facilitate modeling and analysis of large, inhomogeneous, networked dynamical systems. In such systems local dynamics as well as interactions can be described by differential and/or algebraic equations. ND.jl
serves as the technical core of new efforts to develop state of the art power system models in Julia (PowerDynamics.jl).

The aim of this package is to provide the user with a convenient interface that allows them to focus on building models rather than to worry about numerical intricacies. This is achieved by combining the network package LightGraphs.jl with the fully-featured solver suite DifferentialEquations.jl.

Julia turned out to be the perfect environment for our goal since it can be used like a scripting language for protoyping while matching the speed of FORTRAN and C when writing optimized code.

In this talk we introduce the basic constructors of NetworkDynamics.jl and showcase potential applications ranging from neurodynamics to power systems. We conclude with a brief overview of advanced features such as multi-threading, support for stochastic differential equations and integration with the machine learning environment DiffEqFlux.jl. Time Stamps:

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