SUNDIALS

SUNDIALS was implemented with the goal of providing robust time integrators and nonlinear solvers that can easily be incorporated into existing simulation codes. The primary design goals were to require minimal information from the user, allow users to easily supply their own data structures underneath the solvers, and allow for easy incorporation of user-supplied linear solvers and preconditioners. The main numerical operations performed in these codes are operations on data vectors, and the codes have been written in terms of interfaces to these vector operations. The result of this design is that users can relatively easily provide their own data structures to the solvers by telling the solver about their structures and providing the required operations on them. The codes also come with default vector structures with pre-defined operation implementations for both serial and distributed memory parallel environments in case a user prefers not to supply their own structures. In addition, all parallelism is contained within specific vector operations (norms, dot products, etc.) No other operations within the solvers require knowledge of parallelism. Thus, using a solver in parallel consists of using a parallel vector implementation, either the one provided with SUNDIALS, or the user’s own parallel vector structure, underneath the solver. Hence, we do not make a distinction between parallel and serial versions of the codes.


References in zbMATH (referenced in 145 articles , 1 standard article )

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  1. Constantinescu, Emil M.: Generalizing global error estimation for ordinary differential equations by using coupled time-stepping methods (2018)
  2. Edwin Tye, Tom Finnie, Ian Hall, Steve Leach: PyGOM - A Python Package for Simplifying Modelling with Systems of Ordinary Differential Equations (2018) arXiv
  3. Herajy, Mostafa; Liu, Fei; Heiner, Monika: Efficient modelling of yeast cell cycles based on multisite phosphorylation using coloured hybrid Petri nets with marking-dependent arc weights (2018)
  4. Lasagna, Davide: Sensitivity analysis of chaotic systems using unstable periodic orbits (2018)
  5. Oliver Laslett, Jonathon Waters, Hans Fangohr, Ondrej Hovorka: Magpy: A C++ accelerated Python package for simulating magnetic nanoparticle stochastic dynamics (2018) arXiv
  6. Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell: Stan: A Probabilistic Programming Language (2017)
  7. Carsten Burstedde, Jose A. Fonseca, Stefan Kollet: Enhancing speed and scalability of the ParFlow simulation code (2017) arXiv
  8. Einkemmer, Lukas; Tokman, Mayya; Loffeld, John: On the performance of exponential integrators for problems in magnetohydrodynamics (2017)
  9. Flittner, Rudolf; Přibyl, Michal: Computational fluid dynamics model of rhythmic motion of charged droplets between parallel electrodes (2017)
  10. McKenzie, Ross; Pryce, John: Structural analysis based dummy derivative selection for differential algebraic equations (2017)
  11. Melicher, Valdemar; Haber, Tom; Vanroose, Wim: Fast derivatives of likelihood functionals for ODE based models using adjoint-state method (2017)
  12. Nama, Nitesh; Huang, Tony Jun; Costanzo, Francesco: Acoustic streaming: an arbitrary Lagrangian-Eulerian perspective (2017)
  13. Perić, Nikola D.; Villanueva, Mario E.; Chachuat, Beno^it: Sensitivity analysis of uncertain dynamic systems using set-valued integration (2017)
  14. Quirynen, Rien; Gros, Sébastien; Houska, Boris; Diehl, Moritz: Lifted collocation integrators for direct optimal control in ACADO toolkit (2017)
  15. Schroeder, Philipp W.; Lube, Gert: Stabilised DG-FEM for incompressible natural convection flows with boundary and moving interior layers on non-adapted meshes (2017)
  16. Barajas-Solano, David A.; Tartakovsky, Daniel M.: Stochastic collocation methods for nonlinear parabolic equations with random coefficients (2016)
  17. Blom, David S.; Birken, Philipp; Bijl, Hester; Kessels, Fleur; Meister, Andreas; van Zuijlen, Alexander H.: A comparison of Rosenbrock and ESDIRK methods combined with iterative solvers for unsteady compressible flows (2016)
  18. Boiger, R.; Hasenauer, J.; Hroß, S.; Kaltenbacher, B.: Integration based profile likelihood calculation for PDE constrained parameter estimation problems (2016)
  19. Hansen, M.A.; Sutherland, J.C.: Pseudotransient continuation for combustion simulation with detailed reaction mechanisms (2016)
  20. Harwood, Stuart M.; Barton, Paul I.: Efficient polyhedral enclosures for the reachable set of nonlinear control systems (2016)

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