Odetoolbox

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.

Release notes

Version 2.4.1 fixes the computation of analytic solvers for first-order inhomogeneous ODEs.

Citation

Charl Linssen, Pooja N. Babu, Abigail Morrison and Jochen M. Eppler (2021) **ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations.** Zenodo. [doi:10.5281/zenodo.5768597](https://doi.org/10.5281/zenodo.5768597).

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.

Release notes

Version 2.4 adds support to solve first-order inhomogeneous ODEs by exact integration.

Citation

Charl Linssen, Pooja N. Babu, Abigail Morrison and Jochen M. Eppler (2021) **ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations.** Zenodo. [doi:10.5281/zenodo.5768597](https://doi.org/10.5281/zenodo.5768597).

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.

Release notes

Version 2.3 contains various bug fixes and feature enhancements that allow greater control and flexibility in the use of ODE-toolbox.

Citation

Charl Linssen, Shraddha Jain, Abigail Morrison and Jochen M. Eppler (2020) **ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations.** Zenodo. [doi:10.5281/zenodo.4245012](https://doi.org/10.5281/zenodo.4245012).

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.

Release notes

Version 2.2 fixes a bug (43) since the previous release (version 2.1).

Citation

Charl Linssen, Shraddha Jain, Abigail Morrison and Jochen M. Eppler (2020) **ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations.** Zenodo. [doi:10.5281/zenodo.4245012](https://doi.org/10.5281/zenodo.4245012).

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.

Release notes

Version 2.1 fixes several bugs since the previous release (version 2.0).

Citation

Charl Linssen, Shraddha Jain, Abigail Morrison and Jochen M. Eppler (2020) **ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations.** Zenodo. [doi:10.5281/zenodo.4245012](https://doi.org/10.5281/zenodo.4245012).

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.

Charl Linssen, Abigail Morrison and Jochen M. Eppler (2020) **ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations.** Zenodo. [doi:10.5281/zenodo.3822082](https://doi.org/10.5281/zenodo.3822082).