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ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations

Linssen, Charl; Morrison, Abigail; Eppler, Jochen Martin


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3822082", 
  "title": "ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations", 
  "issued": {
    "date-parts": [
      [
        2020, 
        5, 
        28
      ]
    ]
  }, 
  "abstract": "<p>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.</p>\n\n<p>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.</p>", 
  "author": [
    {
      "family": "Linssen, Charl"
    }, 
    {
      "family": "Morrison, Abigail"
    }, 
    {
      "family": "Eppler, Jochen Martin"
    }
  ], 
  "version": "2.0", 
  "type": "article", 
  "id": "3822082"
}
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