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000888401 037__ $$aFZJ-2020-04880
000888401 1001_ $$0P:(DE-Juel1)176305$$aLinssen, Charl$$b0$$eCorresponding author$$ufzj
000888401 245__ $$aODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations
000888401 250__ $$a2.1
000888401 260__ $$c2020
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000888401 500__ $$adoi:10.5281/ZENODO.3822082
000888401 520__ $$aChoosing the optimal solver for systems of ordinary differential equations (ODEs) is a critical step in dynamical systems simulation. ODE-toolbox is a Python package that assists in solver benchmarking, and recommends solvers on the basis of a set of user-configurable heuristics. For all dynamical equations that admit an analytic solution, ODE-toolbox generates propagator matrices that allow the solution to be calculated at machine precision. For all others, first-order update expressions are returned based on the Jacobian matrix.In addition to continuous dynamics, discrete events can be used to model instantaneous changes in system state, such as a neuronal action potential. These can be generated by the system under test, as well as applied as external stimuli, making ODE-toolbox particularly well-suited for applications in computational neuroscience.
000888401 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000888401 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x1
000888401 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2
000888401 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
000888401 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x4
000888401 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x5
000888401 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x6
000888401 7001_ $$0P:(DE-HGF)0$$aJain, Shraddha$$b1
000888401 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b2$$ufzj
000888401 7001_ $$0P:(DE-Juel1)142538$$aEppler, Jochen Martin$$b3$$ufzj
000888401 8564_ $$uhttps://zenodo.org/record/4245012
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000888401 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176305$$aForschungszentrum Jülich$$b0$$kFZJ
000888401 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cologne, Faculty of Mathematics and Natural Sciences, Department of Physics$$b1
000888401 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich$$b2$$kFZJ
000888401 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)142538$$aForschungszentrum Jülich$$b3$$kFZJ
000888401 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000888401 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x1
000888401 9141_ $$y2020
000888401 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000888401 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x1
000888401 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000888401 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x3
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