This title appears in the Scientific Report :
2020
Please use the identifier:
http://dx.doi.org/10.5281/ZENODO.3822082 in citations.
ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations
ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations
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 e...
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Personal Name(s): | Linssen, Charl (Corresponding author) |
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Morrison, Abigail / Eppler, Jochen Martin | |
Contributing Institute: |
Jülich Supercomputing Center; JSC Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Imprint: |
2020
|
DOI: |
10.5281/ZENODO.3822082 |
Document Type: |
Software |
Research Program: |
Human Brain Project Specific Grant Agreement 2 Human Brain Project Specific Grant Agreement 1 Supercomputing and Modelling for the Human Brain Computational Science and Mathematical Methods Theory, modelling and simulation Doktorand ohne besondere Förderung SimLab Neuroscience |
Publikationsportal JuSER |
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. |