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000877336 1001_ $$0P:(DE-Juel1)176305$$aLinssen, Charl$$b0$$eCorresponding author$$ufzj
000877336 245__ $$aODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations
000877336 260__ $$c2020
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000877336 520__ $$aChoosing 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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000877336 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b1$$ufzj
000877336 7001_ $$0P:(DE-Juel1)142538$$aEppler, Jochen Martin$$b2$$ufzj
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