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 237 articles , 1 standard article )

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  1. Ciaran Welsh, Jin Xu, Lucian Smith, Matthias König, Kiri Choi, Herbert M. Sauro: libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis Library (2022) arXiv
  2. Ramirez-Zuniga, Ivan; Rubin, Jonathan. E.; Swigon, David; Redl, Heinz; Clermont, Gilles: A data-driven model of the role of energy in sepsis (2022)
  3. Zhang, Hong; Constantinescu, Emil M.; Smith, Barry F.: \textttPETScTSAdjoint: a discrete adjoint ODE solver for first-order and second-order sensitivity analysis (2022)
  4. Abdelsamie, Abouelmagd; Lartigue, Ghislain; Frouzakis, Christos E.; Thévenin, Dominique: The Taylor-Green vortex as a benchmark for high-fidelity combustion simulations using low-Mach solvers (2021)
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  6. Anton Plietzsch, Raphael Kogler, Sabine Auer, Julia Merino, Asier Gil-de-Muro, Jan Liße, Christina Vogel, Frank Hellmann: PowerDynamics.jl - An experimentally validated open-source package for the dynamical analysis of power grids (2021) arXiv
  7. Arndt, Daniel; Bangerth, Wolfgang; Blais, Bruno; Fehling, Marc; Gassmöller, Rene; Heister, Timo; Heltai, Luca; Köcher, Uwe; Kronbichler, Martin; Maier, Matthias; Munch, Peter; Pelteret, Jean-Paul; Proell, Sebastian; Simon, Konrad; Turcksin, Bruno; Wells, David; Zhang, Jiaqi: The \textttdeal.II library, Version 9.3 (2021)
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  12. Slak, Jure; Kosec, Gregor: Medusa. A C++ library for solving PDEs using strong form mesh-free methods (2021)
  13. Tourigny, David S.: Cooperative metabolic resource allocation in spatially-structured systems (2021)
  14. Arndt, Daniel; Bangerth, Wolfgang; Blais, Bruno; Clevenger, Thomas C.; Fehling, Marc; Grayver, Alexander V.; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Maier, Matthias; Munch, Peter; Pelteret, Jean-Paul; Rastak, Reza; Tomas, Ignacio; Turcksin, Bruno; Wang, Zhuoran; Wells, David: The deal.II library, version 9.2 (2020)
  15. Gros, Sébastien; Zanon, Mario; Quirynen, Rien; Bemporad, Alberto; Diehl, Moritz: From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020)
  16. 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)
  17. Kirches, C.; Lenders, F.; Manns, P.: Approximation properties and tight bounds for constrained mixed-integer optimal control (2020)
  18. Konshin, I. N.; Terekhov, K. M.; Vassilevski, Yu. V.: Numerical modelling via INMOST software platform (2020)
  19. Luo, Ching-Hsing; Chen, Xing-Ji; Chen, Min-Hung: Combination of multi-variable quadratic adaptive algorithm and hybrid operator splitting method for stability against acceleration in the Markov model of sodium ion channels in the ventricular cell model (2020)
  20. Munafò, Alessandro; Alberti, Andrea; Pantano, Carlos; Freund, Jonathan B.; Panesi, Marco: A computational model for nanosecond pulse laser-plasma interactions (2020)

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