A discretization module providing an implementation of mathematical abstractions to solve PDEs on parallel computers including local grid adaptivity, dynamic load balancing, and higher order discretization schemes Discontinuous Galerkin for evolution problems and virtual element methods are available as additional dune modules
Project description
DUNE-FEM
DUNE-FEM is a Distributed and Unified Numerics Environment module which defines interfaces for implementing discretization methods like Finite Element Methods (FEM) and Finite Volume Methods (FV) and Discontinuous Galerkin Methods (DG).
If you need help, please ask on our mailinglist. Bugs can also be submitted to the DUNE-FEM bugtracker instead.
Tutorial
A tutorial for the recently added Python bindings can be found here.
Installation
Using pip
dune-fem can be installed using the Package Index of Python (pip).
pip install dune-fem
See https://dune-project.org/sphinx/content/sphinx/dune-fem/installation.html for a more detailed description.
From source
For a full explanation of the DUNE installation process please read the installation notes.
When using the main branch observe the build status to make sure you get a working version.
Dependencies
DUNE-FEM requires a recent C++ compiler (e.g. g++ or clang), cmake, pkg-config (see DUNE installation for details) and depends on the following DUNE modules:
The following DUNE modules are suggested:
The following software is optional:
License
The DUNE-FEM library, headers and test programs are free open-source software, licensed under version 2 or later of the GNU General Public License.
See the file LICENSE for full copying permissions.
References
A detailed description of DUNE-FEM can be found in
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A. Dedner, R. Klöfkorn, M. Nolte, and M. Ohlberger. A Generic Interface for Parallel and Adaptive Scientific Computing: Abstraction Principles and the DUNE-FEM Module. Computing, 90(3-4):165--196, 2010. http://dx.doi.org/10.1007/s00607-010-0110-3
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A. Dedner, R. Klöfkorn, and M. Nolte. Python Bindings for the DUNE-FEM module. Zenodoo, 2020 http://dx.doi.org/10.5281/zenodo.3706994
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