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NetworkDynamics.jl-Composing and simulating complex networks in Julia.

Michael LindnerLucas LincolnFenja DrauschkeJulia M KoulenHans WürfelAnton PlietzschFrank Hellmann
Published in: Chaos (Woodbury, N.Y.) (2021)
NetworkDynamics.jl is an easy-to-use and computationally efficient package for simulating heterogeneous dynamical systems on complex networks, written in Julia, a high-level, high-performance, dynamic programming language. By combining state-of-the-art solver algorithms from DifferentialEquations.jl with efficient data structures, NetworkDynamics.jl achieves top performance while supporting advanced features such as events, algebraic constraints, time delays, noise terms, and automatic differentiation.
Keyphrases
  • machine learning
  • deep learning
  • high resolution
  • electronic health record
  • mass spectrometry