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.
Keywords for this software
References in zbMATH (referenced in 170 articles , 1 standard article )
Showing results 1 to 20 of 170.
Sorted by year (- Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
- Dallon, J. C.; Leduc, Cécile; Etienne-Manneville, Sandrine; Portet, Stéphanie: Stochastic modeling reveals how motor protein and filament properties affect intermediate filament transport (2019)
- Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
- Alberto Sartori; Nicola Giuliani; Mauro Bardelloni; Luca Heltai: deal2lkit: A toolkit library for high performance programming in deal.II (2018) not zbMATH
- 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)
- 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)
- Bisotti, M.-A., Cortés-Ortuño, D., Pepper, R., Wang, W., Beg, M., Kluyver, T., Fangohr, H.: Fidimag - A Finite Difference Atomistic and Micromagnetic Simulation Package (2018) not zbMATH
- Burstedde, Carsten; Fonseca, Jose A.; Kollet, Stefan: Enhancing speed and scalability of the ParFlow simulation code (2018)
- Buttle, Nicholas R.; Pethiyagoda, Ravindra; Moroney, Timothy J.; McCue, Scott W.: Three-dimensional free-surface flow over arbitrary bottom topography (2018)
- Constantinescu, Emil M.: Generalizing global error estimation for ordinary differential equations by using coupled time-stepping methods (2018)
- Edwin Tye, Tom Finnie, Ian Hall, Steve Leach: PyGOM - A Python Package for Simplifying Modelling with Systems of Ordinary Differential Equations (2018) arXiv
- 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)
- Hernández Pérez, Francisco E.; Mukhadiyev, Nurzhan; Xu, Xiao; Sow, Aliou; Lee, Bok Jik; Sankaran, Ramanan; Im, Hong G.: Direct numerical simulations of reacting flows with detailed chemistry using many-core/GPU acceleration (2018)
- Lasagna, Davide: Sensitivity analysis of chaotic systems using unstable periodic orbits (2018)
- Le, Thuy T. T.; Jost, Felix; Sager, Sebastian: Optimal control of vibration-based micro-energy harvesters (2018)
- Liu, Lulu; Keyes, David E.; Krause, Rolf: A note on adaptive nonlinear preconditioning techniques (2018)
- Maeda, Kazuhiro; Kurata, Hiroyuki: Long negative feedback loop enhances period tunability of biological oscillators (2018)
- Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; Zavala, Victor M.; Biegler, Lorenz T.: \textttpyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations (2018)
- Oliver Laslett, Jonathon Waters, Hans Fangohr, Ondrej Hovorka: Magpy: A C++ accelerated Python package for simulating magnetic nanoparticle stochastic dynamics (2018) arXiv
- Parno, Matthew D.; Marzouk, Youssef M.: Transport map accelerated Markov chain Monte Carlo (2018)