Linssen, Charl
Morrison, Abigail
Eppler, Jochen Martin
2020-05-28
<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>
<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>
https://doi.org/10.5281/zenodo.3822082
oai:zenodo.org:3822082
Zenodo
https://doi.org/10.5281/zenodo.1412608
https://zenodo.org/communities/hbp
https://doi.org/10.5281/zenodo.3822081
info:eu-repo/semantics/openAccess
GNU General Public License v2.0 only
https://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.html
differential equation
ODE
numerical integration
simulation
solver
propagator matrix
symbolic analysis
dynamic system
ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations
info:eu-repo/semantics/other