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@MISC{Linssen:889382,
      author       = {Linssen, Charl and Jain, S. and Morrison, Abigail and
                      Eppler, Jochen Martin},
      title        = {{ODE}-toolbox: {A}utomatic selection and generation of
                      integration schemes for systems of ordinary differential
                      equations; 2.2},
      reportid     = {FZJ-2021-00265},
      year         = {2021},
      abstract     = {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.},
      cin          = {JSC / INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 511 -
                      Computational Science and Mathematical Methods (POF3-511) /
                      5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5232 - Computational
                      Principles (POF4-523) / SMHB - Supercomputing and Modelling
                      for the Human Brain (HGF-SMHB-2013-2017) / HBP SGA2 - Human
                      Brain Project Specific Grant Agreement 2 (785907) / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539) /
                      SLNS - SimLab Neuroscience (Helmholtz-SLNS) / PhD no Grant -
                      Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-511 /
                      G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5232 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)33},
      url          = {https://juser.fz-juelich.de/record/889382},
}