Sundials

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

https://github.com/sciml/sundials.jl

Science Score: 67.0%

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Keywords

arkode clang cvode dae differential-algebraic-equations differential-equations differentialequations hacktoberfest hybrid-differential-equation ida idas julia kinsol ode ordinary-differential-equations scientific-machine-learning sciml sensitivity-analysis sundials sundials-libraries

Keywords from Contributors

numerical sde julialang pde dde neural-ode neural-sde stochastic-differential-equations programming-language partial-differential-equations

Last synced: 6 months ago · JSON representation ·

Repository

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

Basic Info

Host: GitHub
Owner: SciML
License: bsd-2-clause
Language: Julia
Default Branch: master
Homepage: https://diffeq.sciml.ai
Size: 1.76 MB

Statistics

Stars: 210
Watchers: 9
Forks: 79
Open Issues: 40
Releases: 131

Topics

Created almost 13 years ago · Last pushed 6 months ago

Metadata Files

Readme License Citation

README.md

Sundials.jl

Introduction

Sundials.jl is a Julia package that interfaces to the Sundials library (see source). Sundials (the C library and this package) provides the following:

CVODES - for integration and sensitivity analysis of ODEs. CVODES treats stiff and nonstiff ODE systems of the form y' = f(t,y,p), y(t0) = y0(p), where p is a set of parameters.
ARKStep - for integration of non-stiff, stiff, and mixed mode ODEs via split, linearly-implicit form, implicit, and IMEX Runge-Kutta methods on ODEs of the form My' = f_E(t,y,p) + f_i(t,y,p), y(t0) = y0(p) for a set of parameters p.
ERKStep - for integration of non-stiff, stiff, and mixed mode ODEs via split, linearly-implicit form, implicit, and IMEX Runge-Kutta methods on ODEs of the form y' = f(t,y,p), y(t0) = y0(p) for a set of parameters p.
IDAS - for integration and sensitivity analysis of DAEs. IDAS treats DAE systems of the form F(t,y,y',p) = 0, y(t0) = y0(p), y'(t0) = y0'(p)
KINSOL - for solution of nonlinear algebraic systems. KINSOL treats nonlinear systems of the form F(u) = 0

Note that CVODES and IDAS contain all functions provided by CVODE and IDA (for integration without sensitivity analysis). If you need to use the latter, you can set enable_sensitivities=false in deps/build.jl and (re)build the package.

Installation

Within Julia, use the package manager: julia using Pkg Pkg.add("Sundials")

To test the installation use julia using Pkg Pkg.test("Sundials")

Common Interface API

This package is part of the JuliaDiffEq common interface. This is documented in the DifferentialEquations.jl documentation. Thus the ODE tutorial applies. For example, the Lorenz attractor can be solved with CVODE_Adams as follows:

julia using Sundials function lorenz(du,u,p,t) du[1] = 10.0(u[2]-u[1]) du[2] = u[1]*(28.0-u[3]) - u[2] du[3] = u[1]*u[2] - (8/3)*u[3] end u0 = [1.0;0.0;0.0] tspan = (0.0,100.0) prob = ODEProblem(lorenz,u0,tspan) sol = solve(prob,CVODE_Adams()) using Plots; plot(sol,vars=(1,2,3))

Sundials.jl exports the CVODE_BDF, CVODE_Adams, and ARKODE methods for ODEs which are documented in the ODE Solvers page, and IDA which is documented in the DAE solvers page. Additionally, the ARKODE method can be used on SplitODEProblems to solve ODEs in IMEX form.

KINSOL High Level API

Along with the ODE solvers, Sundials contains the KINSOL nonlinear solver. It's called via:

julia kinsol(f, y0::Vector{Float64}; userdata::Any = nothing, linear_solver=:Dense, jac_upper=0, jac_lower=0)

where f(res,y) is an in-place function that computes the residual f(y)-res=0, and KINSOL attempts to find y such that res=0. This method is generally quite fast and the choice linear_solver=:Band is well-suited for problems with a banded Jacobian (you must specify the upper and lower band sizes). However, this is not as robust as many other techniques like trust-region methods, and thus we recommend NLsolve.jl for more general nonlinear solving.

Direct API

This package closely follows the Sundials C API. At a slightly higher level, many (but not all) Sundials.jl functions support passing Julia objects (like Arrays) instead of Sundials objects (like N_Vectors). The Julia package Clang.jl was used to wrap Sundials. This directly uses Sundials' headers sort-of like SWIG. Thus the general C documentation is the documentation for the direct API. See the test directory for usage examples of the direct interface.

For the wrapping code, see src/wrap_sundials.jl. Because of Clang.jl, Sundials.jl provides almost full coverage of the Sundials library (the serial version). A few things to note:

Macros like DENSE_ELEM are not available.
Nothing is exported from the module. You need to put Sundials. in front of everything.
The parallel versions of Sundials which require different N_Vector types have not been wrapped.

Citing

If you use this library, please cite both Sundials and the JuliaDiffEq project.

``` @article{rackauckas2017differentialequations, title={Differentialequations. jl--a performant and feature-rich ecosystem for solving differential equations in julia}, author={Rackauckas, Christopher and Nie, Qing}, journal={Journal of Open Research Software}, volume={5}, number={1}, year={2017}, publisher={Ubiquity Press} }

@article{gardner2022sundials, title={Enabling new flexibility in the {SUNDIALS} suite of nonlinear and differential/algebraic equation solvers}, author={Gardner, David J and Reynolds, Daniel R and Woodward, Carol S and Balos, Cody J}, journal={ACM Transactions on Mathematical Software (TOMS)}, publisher={ACM}, year={2022}, doi={10.1145/3539801} }

@article{hindmarsh2005sundials, title={{SUNDIALS}: Suite of nonlinear and differential/algebraic equation solvers}, author={Hindmarsh, Alan C and Brown, Peter N and Grant, Keith E and Lee, Steven L and Serban, Radu and Shumaker, Dan E and Woodward, Carol S}, journal={ACM Transactions on Mathematical Software (TOMS)}, volume={31}, number={3}, pages={363--396}, year={2005}, publisher={ACM} } ```

Owner

Name: SciML Open Source Scientific Machine Learning
Login: SciML
Kind: organization
Email: contact@chrisrackauckas.com

Website: https://sciml.ai
Twitter: SciML_Org
Repositories: 170
Profile: https://github.com/SciML

Open source software for scientific machine learning

Citation (CITATION.bib)

@article{DifferentialEquations.jl-2017,
 author = {Rackauckas, Christopher and Nie, Qing},
 doi = {10.5334/jors.151},
 journal = {The Journal of Open Research Software},
 keywords = {Applied Mathematics},
 note = {Exported from https://app.dimensions.ai on 2019/05/05},
 number = {1},
 pages = {},
 title = {DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia},
 url = {https://app.dimensions.ai/details/publication/pub.1085583166 and http://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/galley/245/download/},
 volume = {5},
 year = {2017}
}

GitHub Events

Total

Create event: 14
Commit comment event: 2
Release event: 2
Issues event: 4
Watch event: 3
Delete event: 13
Issue comment event: 53
Push event: 24
Pull request review comment event: 3
Pull request review event: 4
Pull request event: 39
Fork event: 3

Last Year

Create event: 14
Commit comment event: 2
Release event: 2
Issues event: 4
Watch event: 3
Delete event: 13
Issue comment event: 53
Push event: 24
Pull request review comment event: 3
Pull request review event: 4
Pull request event: 39
Fork event: 3

Committers

Last synced: 8 months ago

All Time

Total Commits: 944
Total Committers: 75
Avg Commits per committer: 12.587
Development Distribution Score (DDS): 0.493

Past Year

Commits: 29
Committers: 6
Avg Commits per committer: 4.833
Development Distribution Score (DDS): 0.655

Top Committers

Name	Email	Commits
Christopher Rackauckas	C**t@C**m	479
Jose Daniel Lara	j**a@b**u	121
oscarddssmith	o**h@j**m	47
Yingbo Ma	m**5@g**m	32
rodrigomha	r**a@g**m	26
Alexey Stukalov	a**v@g**m	26
Tony Kelman	t**y@k**t	16
Tom Short	t**t@i**g	15
Daines	S**s@e**k	12
Tom Short	t**s@g**m	12
dependabot[bot]	4****]	7
acroy	a**y@g**m	7
Avik Pal	a**l@m**u	7
ScottPJones	s**s@a**u	5
github-actions[bot]	4****]	5
Sam Isaacson	i****s	5
Elliot Saba	s**t@g**m	5
Keno Fischer	k**o@j**m	4
Takafumi Arakaki	a**f@g**m	4
Kanav Gupta	k**0@g**m	4
Dominique Luna	d**2@g**m	4
Anant Thazhemadam	a**m@g**m	4
David Widmann	d****n	4
Utkarsh	r**0@g**m	4
Anas_Abdelrehim	a**r@g**m	4
Luke Stagner	l**r@u**u	4
Pepijn de Vos	p**s@j**m	4
Stuart Daines	s**s@g**m	4
melonedo	4****o	3
lcontento	l****o	3
and 45 more...

Committer Domains (Top 20 + Academic)

juliacomputing.com: 3 mit.edu: 2 chrisrackauckas.com: 1 berkeley.edu: 1 kelman.net: 1 ieee.org: 1 exeter.ac.uk: 1 alum.mit.edu: 1 uci.edu: 1 soton.ac.uk: 1 julialang.org: 1 ranocha.de: 1 ciencias.unam.mx: 1 comcast.net: 1 cdg.dev: 1 umich.edu: 1 nyu.edu: 1 gnu.org: 1 runbox.com: 1 simons-rock.edu: 1 llnl.gov: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 71
Total pull requests: 133
Average time to close issues: 8 months
Average time to close pull requests: 14 days
Total issue authors: 47
Total pull request authors: 31
Average comments per issue: 7.38
Average comments per pull request: 1.5
Merged pull requests: 101
Bot issues: 0
Bot pull requests: 13

Past Year

Issues: 4
Pull requests: 28
Average time to close issues: 3 days
Average time to close pull requests: about 9 hours
Issue authors: 4
Pull request authors: 9
Average comments per issue: 1.75
Average comments per pull request: 0.64
Merged pull requests: 13
Bot issues: 0
Bot pull requests: 2

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Top Authors

Issue Authors

sjdaines (7)
ViralBShah (4)
portig (3)
jd-lara (3)
ChrisRackauckas (3)
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SebastianM-C (2)
JianghuiDu (2)
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ufechner7 (2)
oscardssmith (2)
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topolarity (2)
japhir (1)
jguterl (1)

Pull Request Authors

ChrisRackauckas (42)
oscardssmith (17)
dependabot[bot] (12)
sjdaines (9)
avik-pal (7)
ViralBShah (6)
jd-lara (4)
pepijndevos (3)
thazhemadam (3)
vpuri3 (2)
Keno (2)
AayushSabharwal (2)
melonedo (2)
domluna (2)
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bug (7) question (1)

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Packages

Total packages: 1
Total downloads:
- julia 2,918 total

Total dependent packages: 32
Total dependent repositories: 23
Total versions: 96

juliahub.com: Sundials

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

Homepage: https://diffeq.sciml.ai
Documentation: https://docs.juliahub.com/General/Sundials/stable/
License: BSD-2-Clause
Latest release: 4.28.0
published 11 months ago

Versions: 96
Dependent Packages: 32
Dependent Repositories: 23
Downloads: 2,918 Total

Rankings

Forks count: 1.4%

Dependent repos count: 1.6%

Dependent packages count: 2.3%

Average: 2.3%

Stargazers count: 3.9%

Last synced: 6 months ago

Dependencies

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