A mesh generator using machine learning. In particular the software uses the Kohonen self organizing map
based on the work of T. Kohonen [1] and O. Nechaeva [2] where some input structured or unstructured mesh is trained to represent the input geometry. The following animation shows the learning process for a NACA geometry:
Currently this project is in a pre-alpha/conceptional stage and has a few limitations:
- limited speed especially for large meshes
- only unstructured triangular meshes: it is possible to use this algorithm for structured meshes since the mesh topology remains
- adaptive meshes are not implemented
- grid quality can't be guaranteed
- only 2-dimensionsal
The software is installed by executing the command pip install -e . in the main folder.
The source for this project is available here.
After installing, the two examples in the examples folder can be started by executing
python grgen_example_naca.py or python grgen_example_sphere.py.
The exact usage of the package is documented in these example files.
[1] Kohonen, T. (2012). Self-organizing maps. Communications of the ACM, 11(3), 147-148. Springer Science & Business Media V.30, Springer.
[2] Nechaeva, O. (2006). Composite algorithm for adaptive mesh construction based on self-organizing maps. International Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, 147-148.