SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers

By A. Hindmarsh, Peter N. Brown, K. Grant, Steven L. Lee, Radu Serban, D. Shumaker, Carol S. Woodward. 2005

1,468 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Numerical Analysis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computational Theory and Mathematics

In Numerical Analysis

Citations in the corpus

Consistent modelling of the meta-galactic UV background and the thermal/ionization history of the intergalactic medium (2018, methodology): “…below, we computationally efficiently integrate this set of stiff ordinary differential equations with the CVODE library2 (Cohen et al. 1996; Hindmarsh et al. 2005), while for predicting the evolution of the IGM properties at mean cosmic density we also use a simpler code that performs a…”
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++ (2015, methodology): “More robust integrators, in particular the CVODE integrator (Cohen and Hindmarsh, 1996) from the SUNDIALS package (Hindmarsh et al., 2005), will be useful for their ability to automatically deal with stiff systems of equations.”
Lessons Learned from Quantitative Dynamical Modeling in Systems Biology (2013, methodology): “An efficient algorithm for solving the enlarged ODE system is the CVODES solver [14].”
CellDesigner 3.5: A Versatile Modeling Tool for Biochemical Networks (2008, methodology): “The SBML ODE Solver uses SUNDIALS CVODES [ 15 ] for numerical integration and sensitivity analysis.”
Diversitree: comparative phylogenetic analyses of diversification in R (2012, methodology): “e "cvodes" backend uses the cvodes algorithm (Cohen & Hindemarsh, 1996) from the sundials library of solvers (Hindmarsh et al., 2005)1.”
Sensitivity analysis approaches applied to systems biology models. (2011, methodology): “The sensitivity of ODE system (6) can be numerically solved with the CVODES solver in the SUNDIALS library [14, 15].”
Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity (2017, methodology): “Virtual Assay uses the ordinary diﬀerential equation solver CVODE, part of the open-source Sundials suite (Hindmarsh et al., 2005; Serban and Hindmarsh, 2005), implementing time adaptive Backward Diﬀerentiation Formulas with relative and absolute tolerance equal to 1e-5 and 1e-7, respectively.”
Identifying Causal Structure in Large-Scale Kinetic Systems (2018, methodology): “Their numerical integration is based on CVODES of the SUNDIALS suite [Hindmarsh et al., 2005].”
DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia (2017, methodology): “Because of the ubiquity of these equations, standard sets of solvers have been developed, including Shampine’s ODE suite for MATLAB [25], Hairer’s Fortran codes [8], and the Sundials CVODE solvers [11].”
Predicting ligand-dependent tumors from multi-dimensional signaling features (2017, methodology): “In this work, the dynamical system and its sensitivities were integrated by the CVODES integrator of the SUNDIALS suite.(71) Therein, an implicit BDF integration method(72) with attached KLU sparse solver was chosen.”
Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems (2015, methodology): “In the presented software package, the CVODES solver (Hindmarsh et al., 2005) is used.”
Enabling New Flexibility in the SUNDIALS Suite of Nonlinear and Differential/Algebraic Equation Solvers (2020, background): “vector structure [41] has enabled incorporating new HPC programming models [49, 63].”