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Published December 3, 2020 | Version 2.1
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ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations

  • 1. Simulation Lab Neuroscience, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich, Germany
  • 2. University of Cologne, Faculty of Mathematics and Natural Sciences, Department of Physics
  • 3. Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), Jülich Aachen Research Alliance BRAIN Institute I, Forschungszentrum Jülich, Germany

Description

Choosing the optimal solver for systems of ordinary differential equations (ODEs) is a critical step in dynamical systems simulation. ODE-toolbox is a Python package that assists in solver benchmarking, and recommends solvers on the basis of a set of user-configurable heuristics. For all dynamical equations that admit an analytic solution, ODE-toolbox generates propagator matrices that allow the solution to be calculated at machine precision. For all others, first-order update expressions are returned based on the Jacobian matrix.

In addition to continuous dynamics, discrete events can be used to model instantaneous changes in system state, such as a neuronal action potential. These can be generated by the system under test, as well as applied as external stimuli, making ODE-toolbox particularly well-suited for applications in computational neuroscience.

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Related works

Is cited by
Software: 10.5281/zenodo.1412608 (DOI)
Is new version of
Software: 10.5281/zenodo.3822082 (DOI)