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ODEInterfaceDiffEq

Adds the common API onto ODEInterface classic Fortran methods for the SciML Scientific Machine Learning organization

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

Science Score: 36.0%

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    CITATION.cff file
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    Committers with academic emails
    4 of 24 committers (16.7%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (14.0%) to scientific vocabulary

Keywords

differential-equations hairer julia radau rodas scientific-machine-learning sciml

Keywords from Contributors

pde ode differentialequations dae sde differential-algebraic-equations neural-differential-equations matrix-exponential neural-ode stochastic-differential-equations
Last synced: 9 months ago · JSON representation

Repository

Adds the common API onto ODEInterface classic Fortran methods for the SciML Scientific Machine Learning organization

Basic Info
  • Host: GitHub
  • Owner: SciML
  • License: other
  • Language: Julia
  • Default Branch: master
  • Homepage:
  • Size: 218 KB
Statistics
  • Stars: 8
  • Watchers: 5
  • Forks: 16
  • Open Issues: 4
  • Releases: 40
Topics
differential-equations hairer julia radau rodas scientific-machine-learning sciml
Created over 9 years ago · Last pushed 10 months ago
Metadata Files
Readme License Citation

README.md

ODEInterfaceDiffEq

Join the chat at https://gitter.im/JuliaDiffEq/Lobby Build Status Coverage Status codecov.io

This package contains bindings for ODEInterface.jl to allow it to be used with the JuliaDiffEq common interface. For more information on using the solvers from this package, see the DifferentialEquations.jl documentation.

Installation

A standard installation on MacOSX and Linux should work. On Windows, you need to install mingw32 compilers and add them to the path. MingW32 can be found here. Then add the path to your environment variables. An example path is:

C:\Program Files\mingw-w64\x86_64-6.1.0-posix-seh-rt_v5-rev0\mingw64\bin

Note that it is required that you add ODEInterface.jl as well;

julia ]add ODEInterface

Otherwise you may have issues instantiating the solvers.

Common API Usage

This library adds the common interface to ODEInterface.jl's solvers. See the DifferentialEquations.jl documentation for details on the interface. Following the Lorenz example from the ODE tutorial, we can solve this using dopri5 via the following:

julia using ODEInterface, ODEInterfaceDiffEq 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,dopri5(),abstol=1e-4) using Plots; plot(sol,vars=(1,2,3))

The options available in solve are documented at the common solver options page. The available methods are documented at the ODE solvers page.

Owner

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

Open source software for scientific machine learning

GitHub Events

Total
  • Release event: 1
  • Delete event: 4
  • Issue comment event: 3
  • Push event: 8
  • Pull request event: 10
  • Fork event: 1
  • Create event: 6
Last Year
  • Release event: 1
  • Delete event: 4
  • Issue comment event: 3
  • Push event: 8
  • Pull request event: 10
  • Fork event: 1
  • Create event: 6

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 188
  • Total Committers: 24
  • Avg Commits per committer: 7.833
  • Development Distribution Score (DDS): 0.303
Past Year
  • Commits: 10
  • Committers: 4
  • Avg Commits per committer: 2.5
  • Development Distribution Score (DDS): 0.5
Top Committers
Name Email Commits
Christopher Rackauckas C****t@C****m 131
Yingbo Ma m****5@g****m 9
dependabot[bot] 4****] 7
github-actions[bot] 4****] 7
Anant Thazhemadam a****m@g****m 5
ScottPJones s****s@a****u 4
David Widmann d****n 4
Anas_Abdelrehim a****r@g****m 2
Chris de Graaf me@c****v 2
George Datseris d****e@g****m 2
Hendrik Ranocha h****a@t****e 2
Julia TagBot 5****t 1
Kanav Gupta k****0@g****m 1
Elliot Saba s****t@g****m 1
Benjamin Chung c****e@g****m 1
Anshul Singhvi a****7@s****u 1
Thomas Vetter 8****t 1
Unknown v****t@g****m 1
Xingjian Guo x****3@n****u 1
femtocleaner[bot] f****] 1
Ivan Borisov i****v@g****m 1
Pepijn de Vos p****s@j****m 1
CompatHelper Julia c****y@j****g 1
sjdaines s****s@g****m 1
Committer Domains (Top 20 + Academic)
julialang.org: 1 juliacomputing.com: 1 nyu.edu: 1 simons-rock.edu: 1 tu-bs.de: 1 cdg.dev: 1 alum.mit.edu: 1 chrisrackauckas.com: 1

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 11
  • Total pull requests: 57
  • Average time to close issues: 8 months
  • Average time to close pull requests: 6 days
  • Total issue authors: 7
  • Total pull request authors: 23
  • Average comments per issue: 9.91
  • Average comments per pull request: 0.46
  • Merged pull requests: 57
  • Bot issues: 0
  • Bot pull requests: 16
Past Year
  • Issues: 0
  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: about 5 hours
  • Issue authors: 0
  • Pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • ChrisRackauckas (3)
  • ktitimbo (2)
  • JianghuiDu (2)
  • dburov190 (1)
  • ivborissov (1)
  • MSeeker1340 (1)
  • JuliaTagBot (1)
Pull Request Authors
  • ChrisRackauckas (17)
  • dependabot[bot] (9)
  • github-actions[bot] (8)
  • ScottPJones (4)
  • thazhemadam (4)
  • devmotion (3)
  • YingboMa (3)
  • christopher-dG (2)
  • ranocha (2)
  • sjdaines (1)
  • JuliaTagBot (1)
  • pepijndevos (1)
  • ivborissov (1)
  • AnasAbdelR (1)
  • BenChung (1)
Top Labels
Issue Labels
question (1)
Pull Request Labels
dependencies (9)

Packages

  • Total packages: 1
  • Total downloads:
    • julia 27 total
  • Total dependent packages: 1
  • Total dependent repositories: 0
  • Total versions: 31
juliahub.com: ODEInterfaceDiffEq

Adds the common API onto ODEInterface classic Fortran methods for the SciML Scientific Machine Learning organization

  • Versions: 31
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 27 Total
Rankings
Forks count: 9.3%
Dependent repos count: 9.9%
Average: 20.0%
Dependent packages count: 23.0%
Stargazers count: 37.7%
Last synced: 9 months ago

Dependencies

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