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@MISC{Linssen:1041472,
      author       = {Linssen, Charl and Jain, Shraddha and Babu, Pooja and
                      Morrison, Abigail and Eppler, Jochen Martin},
      title        = {{ODE}-toolbox 2.5.8; 2.5.8},
      reportid     = {FZJ-2025-02265},
      year         = {2025},
      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 / IAS-6},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5234 - Emerging NC
                      Architectures (POF4-523) / 5235 - Digitization of
                      Neuroscience and User-Community Building (POF4-523) / 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) / EBRAINS 2.0 - EBRAINS 2.0: A
                      Research Infrastructure to Advance Neuroscience and Brain
                      Health (101147319)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5234 /
                      G:(DE-HGF)POF4-5235 / G:(EU-Grant)945539 /
                      G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405 / G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)33},
      url          = {https://juser.fz-juelich.de/record/1041472},
}