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| Home > Publications database > ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations > print |
| Home > Publications database > ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations > print |
| 001 | 889382 | ||
| 005 | 20240313094933.0 | ||
| 037 | _ | _ | |a FZJ-2021-00265 |
| 100 | 1 | _ | |a Linssen, Charl |0 P:(DE-Juel1)176305 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations |
| 250 | _ | _ | |a 2.2 |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Software |2 DCMI |
| 336 | 7 | _ | |a Software |b sware |m sware |0 PUB:(DE-HGF)33 |s 1634739615_19757 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Computer Program |0 6 |2 EndNote |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a Software |2 DataCite |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 |
| 536 | _ | _ | |a 511 - Computational Science and Mathematical Methods (POF3-511) |0 G:(DE-HGF)POF3-511 |c POF3-511 |f POF III |x 1 |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 2 |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 3 |
| 536 | _ | _ | |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) |0 G:(DE-Juel1)HGF-SMHB-2013-2017 |c HGF-SMHB-2013-2017 |f SMHB |x 4 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 5 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 6 |
| 536 | _ | _ | |a SLNS - SimLab Neuroscience (Helmholtz-SLNS) |0 G:(DE-Juel1)Helmholtz-SLNS |c Helmholtz-SLNS |x 7 |
| 536 | _ | _ | |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |c PHD-NO-GRANT-20170405 |x 8 |
| 700 | 1 | _ | |a Jain, S. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Morrison, Abigail |0 P:(DE-Juel1)151166 |b 2 |u fzj |
| 700 | 1 | _ | |a Eppler, Jochen Martin |0 P:(DE-Juel1)142538 |b 3 |u fzj |
| 856 | 4 | _ | |u https://doi.org/10.5281/zenodo.4245012 |
| 909 | C | O | |o oai:juser.fz-juelich.de:889382 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)176305 |
| 910 | 1 | _ | |a University of Cologne, Faculty of Mathematics and Natural Sciences, Department of Physics |0 I:(DE-HGF)0 |b 1 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)151166 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)142538 |
| 913 | 0 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Theory, modelling and simulation |x 0 |
| 913 | 0 | _ | |a DE-HGF |b Key Technologies |l Supercomputing & Big Data |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Computational Science and Mathematical Methods |x 1 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5232 |x 1 |
| 914 | 1 | _ | |y 2021 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 2 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 3 |
| 980 | _ | _ | |a sware |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
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