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Grid-based approximation of partial differential equations in Julia

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Gridap

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Arxiv: 1910.01412 Build Status Codecov Join the chat at https://gitter.im/Gridap-jl/community

Gridap provides a set of tools for the grid-based approximation of partial differential equations (PDEs) written in the Julia programming language. The main motivation behind the development of this library is to provide an easy-to-use framework for the development of complex PDE solvers in a dynamically typed style without sacrificing the performance of statically typed languages. The library currently supports linear and nonlinear PDE systems for scalar and vector fields, single and multi-field problems, conforming and nonconforming finite element discretizations, on structured and unstructured meshes of simplices and hexahedra.

Documentation

  • STABLEDocumentation for the most recently tagged version of Gridap.jl.
  • DEVELDocumentation for the in-development version of Gridap.
  • ARTICLE — F. Verdugo, S. Badia. A user-guide to Gridap -- grid-based approximation of partial differential equations in Julia. arXiv. 2019. arXiv:1910.01412

Installation

Gridap is a registered package in the official Julia package registry. Thus, the installation of Gridap is straight forward using the Julia's package manager. Open the Julia REPL, type ] to enter package mode, and install as follows

pkg> add Gridap

Tutorials

A hands-on user-guide to the library is available as a set of tutorials. They are available as Jupyter notebooks and html pages.

Plugins

Examples

These are some popular PDEs solved with the Gridap library. Examples taken from the Gridap Tutorials.

Poisson equation Linear elasticity Hyper-elasticity p-Laplacian
Poisson eq. with DG Darcy eq. with RT Incompressible Navier-Stokes Isotropic damage

How to cite Gridap

If you have used the Gridap library in a scientific publication, please cite the project as follows:

@article{gridap_guide_2019,
    author={Francesc Verdugo and Santiago Badia},
    journal = {{arXiv}},
    title = {{A user-guide to Gridap -- grid-based approximation of partial differential equations in Julia}},
    year = {2019},
    eprint={1910.01412},
    archivePrefix={arXiv},
    primaryClass={cs.MS},
}

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