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

Wrappers for GeometricIntegrators.jl into the SciML common interface for scientific machine learning (SciML)
https://github.com/sciml/geometricintegratorsdiffeq.jl

differential-equations geometric-algorithms geometricintegrators scientific-machine-learning sciml

Last synced: about 1 month ago
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Wrappers for GeometricIntegrators.jl into the SciML common interface for scientific machine learning (SciML)

• Host: GitHub
• URL: https://github.com/sciml/geometricintegratorsdiffeq.jl
• Owner: SciML
• License: other
• Created: 2017-11-24T15:49:49.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2025-08-18T03:06:41.000Z (about 2 months ago)
• Last Synced: 2025-08-28T17:52:59.147Z (about 1 month ago)
• Topics: differential-equations, geometric-algorithms, geometricintegrators, scientific-machine-learning, sciml
• Language: Julia
• Homepage:
• Size: 86.9 KB
• Stars: 8
• Watchers: 2
• Forks: 12
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Citation: CITATION.bib

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README

          
# GeometricIntegratorsDiffEq.jl

[![Build Status](https://github.com/SciML/GeometricIntegratorsDiffEq.jl/workflows/CI/badge.svg)](https://github.com/SciML/GeometricIntegratorsDiffEq.jl/actions?query=workflow%3ACI)

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[![codecov.io](http://codecov.io/github/JuliaDiffEq/GeometricIntegratorsDiffEq.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaDiffEq/GeometricIntegratorsDiffEq.jl?branch=master)

This package contains bindings for GeometricIntegrators.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](https://diffeq.sciml.ai/stable/).

## Common API Usage

This library adds the common interface to GeometricIntegrators.jl's solvers. [See the DifferentialEquations.jl documentation for details on the interface](https://diffeq.sciml.ai/stable/index.html). Following the Lorenz example from [the ODE tutorial](https://diffeq.sciml.ai/stable/tutorials/ode_example/), we can solve this using `GIEuler` via the following:

```julia

using GeometricIntegratorsDiffEq

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,GIEuler(),dt=0.1)

using Plots; plot(sol,vars=(1,2,3))

```

The options available in `solve` are documented [at the common solver options page](https://diffeq.sciml.ai/stable/basics/common_solver_opts/). The available methods are documented [at the ODE solvers page](https://diffeq.sciml.ai/stable/solvers/ode_solve#GeometricIntegrators.jl-1).