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 220 articles , 1 standard article )

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  1. Anderson, Robert; Andrej, Julian; Barker, Andrew; Bramwell, Jamie; Camier, Jean-Sylvain; Cerveny, Jakub; Dobrev, Veselin; Dudouit, Yohann; Fisher, Aaron; Kolev, Tzanio; Pazner, Will; Stowell, Mark; Tomov, Vladimir; Akkerman, Ido; Dahm, Johann; Medina, David; Zampini, Stefano: MFEM: a modular finite element methods library (2021)
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  3. Hegedűs, Ferenc: Program package MPGOS: challenges and solutions during the integration of a large number of independent ODE systems using GPUs (2021)
  4. Tourigny, David S.: Cooperative metabolic resource allocation in spatially-structured systems (2021)
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  7. Jiang, Canghua; Guo, Zhiqiang; Li, Xin; Wang, Hai; Yu, Ming: An efficient adjoint computational method based on lifted IRK integrator and exact penalty function for optimal control problems involving continuous inequality constraints (2020)
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  9. Munafò, Alessandro; Alberti, Andrea; Pantano, Carlos; Freund, Jonathan B.; Panesi, Marco: A computational model for nanosecond pulse laser-plasma interactions (2020)
  10. Rodrigo Henriquez-Auba, Jose D. Lara, Ciaran Roberts, Nathan Pallo, Duncan S. Callaway: LITS.jl - An Open-Source Julia based Simulation Toolbox for Low-Inertia Power Systems (2020) arXiv
  11. Schmiester, Leonard; Weindl, Daniel; Hasenauer, Jan: Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach (2020)
  12. Shen, Kai; Scott, Joseph K.: Exploiting nonlinear invariants and path constraints to achieve tighter reachable set enclosures using differential inequalities (2020)
  13. Takeshi Abe; Yoshiyuki Asai: Flint: a simulator for biological and physiological models in ordinary and stochastic differential equations (2020) not zbMATH
  14. Tourigny, David S.: Dynamic metabolic resource allocation based on the maximum entropy principle (2020)
  15. Zheltkova, V. V.; Zheltkov, Dmitry A.; Bocharov, G. A.; Tyrtyshnikov, Eugene: Application of the global optimization methods for solving the parameter estimation problem in mathematical immunology (2020)
  16. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  17. An, Hengbin; Mo, Zeyao; Xu, Xiaowen; Jia, Xiaowei: Operator-based preconditioning for the 2-D 3-T energy equations in radiation hydrodynamics simulations (2019)
  18. Arndt, Daniel; Bangerth, Wolfgang; Clevenger, Thomas C.; Davydov, Denis; Fehling, Marc; Garcia-Sanchez, Daniel; Harper, Graham; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Kynch, Ross Maguire; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, Version 9.1 (2019)
  19. Boukharfane, Radouan; Martínez Ferrer, Pedro José; Mura, Arnaud; Giovangigli, Vincent: On the role of bulk viscosity in compressible reactive shear layer developments (2019)
  20. Chernyshenko, Alexey Y.; Danilov, A. A.; Vassilevski, Y. V.: Numerical simulations for cardiac electrophysiology problems (2019)

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