Waves is a collection of general utilities and finite element tools for solving various PDE. The code is written in C++ interfaced in python through SWIG, and is developed at the University of Liège.
# Katoptron
Themomechanical Finite Element solver based on [Trilinos](https://trilinos.github.io/) from Kim Liegeois's Phd thesis.
## Features
Set of python/C++ modules:
-[katoptron](/katoptron): 3D(Hex8 and Tetra4) linear thermomechanical equations with contact (FEM)
-[tlnos](/tlnos): basic Trilinos examples
This code was previously part of [waves](https://gitlab.uliege.be/am-dept/waves).
Additional features:
-[x] [Gmsh](http://geuz.org/gmsh/) support for mesh and post-processing
-[x] [VTK](http://www.vtk.org/)/[PyQt](https://riverbankcomputing.com/software/pyqt/intro) support for post processing and basic GUI
-[x] [OpenBLAS](http://www.openblas.net/) or [Intel MKL](https://software.intel.com/en-us/intel-mkl) support
-[x] [Eigen3](http://eigen.tuxfamily.org/index.php?title=Main_Page) support for linear algebra
-[Intel Pardiso](https://software.intel.com/content/www/us/en/develop/documentation/mkl-developer-reference-c/top/sparse-solver-routines/intel-mkl-pardiso-parallel-direct-sparse-solver-interface/pardiso.html) support
-[MUMPS](http://mumps.enseeiht.fr/) support
-[x] [TBB](https://www.threadingbuildingblocks.org/) support for multithreading
-[x] [mpi4py](https://bitbucket.org/mpi4py/mpi4py) support for parallelization
-[x] [Trilinos](https://trilinos.github.io/) support (for some modules)
## Build
Detailed build instructions can be found in the [wiki](https://gitlab.uliege.be/am-dept/waves/wikis/home).
## References
Liegeois Kim, [GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models with application to the thermomechanical simulation of an ITER front mirror](http://hdl.handle.net/2268/249334), University of Liège, 2020.
### References
Liegeois K., [GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models with application to the thermomechanical simulation of an ITER front mirror](http://hdl.handle.net/2268/249334), University of Liège, 2020.
Liegeois K., Boman R., Phipps E., Wiesner T., and Arnst M., [GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models](http://hdl.handle.net/2268/248201), Computer Methods in Applied Mechanics and Engineering, Vol. 369, 2020.
