juliaodedifferential-equationsdifferentialequations.jl

Decoupled ODEs in Julia


The Lorenz attractor is the archetypal example in Julia documentation for solving systems of ODEs using DifferentialEquations.jl. Suppose I would like to solve that system for one thousand different initial conditions, keeping the attractor parameters fixed. What would be a good (i.e. efficient) way to solve and store the data for these 1000 decoupled ODEs?


Solution

  • Answering my own question...

    A very interesting option is to work with ensembles:

    https://diffeq.sciml.ai/stable/features/ensemble/

    This feature offers support for various data analysis as well as good performance by using multi thread functionality. Should be a good starting point for anyone interested in running what in the original question should be called an Ensemble.