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000907647 037__ $$aFZJ-2022-02126
000907647 1001_ $$0P:(DE-Juel1)176305$$aLinssen, Charl$$b0$$eCorresponding author$$ufzj
000907647 245__ $$aODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations
000907647 260__ $$c2022
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000907647 520__ $$aChoosing the optimal solver for systems of ordinary differential equations (ODEs) is a critical step in dynamical systems simulation. ODE-toolbox is a Python package that assists in solver benchmarking, and recommends solvers on the basis of a set of user-configurable heuristics. For all dynamical equations that admit an analytic solution, ODE-toolbox generates propagator matrices that allow the solution to be calculated at machine precision. For all others, first-order update expressions are returned based on the Jacobian matrix.In addition to continuous dynamics, discrete events can be used to model instantaneous changes in system state, such as a neuronal action potential. These can be generated by the system under test, as well as applied as external stimuli, making ODE-toolbox particularly well-suited for applications in computational neuroscience.
000907647 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000907647 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000907647 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x2
000907647 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x3
000907647 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x4
000907647 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x5
000907647 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x6
000907647 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x7
000907647 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x8
000907647 7001_ $$0P:(DE-Juel1)186954$$aBabu, Pooja$$b1$$ufzj
000907647 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b2$$ufzj
000907647 7001_ $$0P:(DE-Juel1)142538$$aEppler, Jochen Martin$$b3$$ufzj
000907647 8564_ $$uhttps://doi.org/10.5281/zenodo.6533853
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000907647 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
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000907647 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x3
000907647 9141_ $$y2022
000907647 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000907647 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x1
000907647 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000907647 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x3
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