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 202 articles , 1 standard article )

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  1. Gros, Sébastien; Zanon, Mario; Quirynen, Rien; Bemporad, Alberto; Diehl, Moritz: From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020)
  2. 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)
  3. Kirches, C.; Lenders, F.; Manns, P.: Approximation properties and tight bounds for constrained mixed-integer optimal control (2020)
  4. Shen, Kai; Scott, Joseph K.: Exploiting nonlinear invariants and path constraints to achieve tighter reachable set enclosures using differential inequalities (2020)
  5. Tourigny, David S.: Dynamic metabolic resource allocation based on the maximum entropy principle (2020)
  6. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  7. 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)
  8. Boukharfane, Radouan; Martínez Ferrer, Pedro José; Mura, Arnaud; Giovangigli, Vincent: On the role of bulk viscosity in compressible reactive shear layer developments (2019)
  9. Chernyshenko, Alexey Y.; Danilov, A. A.; Vassilevski, Y. V.: Numerical simulations for cardiac electrophysiology problems (2019)
  10. Dallon, J. C.; Evans, Emily J.; Grant, Christopher P.; Smith, W. V.: Results from a differential equation model for cell motion with random switching show that the model cell velocity is asymptotically independent of force (2019)
  11. Dallon, J. C.; Leduc, Cécile; Etienne-Manneville, Sandrine; Portet, Stéphanie: Stochastic modeling reveals how motor protein and filament properties affect intermediate filament transport (2019)
  12. Dudley, Harry J.; Lu, Lu; Ren, Zhiyong Jason; Bortz, David M.: Sensitivity and bifurcation analysis of a differential-algebraic equation model for a microbial electrolysis cell (2019)
  13. Dudziuk, Grzegorz; Wronowska, Weronika; Gambin, Anna; Szymańska, Zuzanna; Rybiński, Mikołaj: Biologically sound formal model of Hsp70 heat induction (2019)
  14. Farrell, P. E.; Hake, J. E.; Funke, S. W.; Rognes, M. E.: Automated adjoints of coupled PDE-ODE systems (2019)
  15. Kuntz, Juan; Thomas, Philipp; Stan, Guy-Bart; Barahona, Mauricio: The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains (2019)
  16. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  17. Simon O’Meara; Shuxuan Xu; David Topping; Gerard Capes; Douglas Lowe; M. Rami Alfarra; Gordon McFiggans: PyCHAM: CHemistry with Aerosol Microphysics in Python (2019) not zbMATH
  18. Alberto Sartori; Nicola Giuliani; Mauro Bardelloni; Luca Heltai: deal2lkit: A toolkit library for high performance programming in deal.II (2018) not zbMATH
  19. Alzetta, Giovanni; Arndt, Daniel; Bangerth, Wolfgang; Boddu, Vishal; Brands, Benjamin; Davydov, Denis; Gassmöller, Rene; Heister, Timo; Heltai, Luca; Kormann, Katharina; Kronbichler, Martin; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, version 9.0 (2018)
  20. Bazzazi, Hojjat; Zhang, Yu; Jafarnejad, Mohammad; Popel, Aleksander S.: Computational modeling of synergistic interaction between (\alpha)V(\beta)3 integrin and VEGFR2 in endothelial cells: implications for the mechanism of action of angiogenesis-modulating integrin-binding peptides (2018)

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