Python library for Rational Reduced Order Modeling
Bumped version to 2.1
Fixed non-existent muBounds bug. Added back corrector steps at end of greedy…
Many changes in import visibility.
Updated examples/tests with new potential routine. Boosted results of some…
Improved potential computations through ground potential and more accurate foci.
Added generic random parameter samplers. Added foci and ground potential values…
Added Wendland radial basis support.
Fixed depth issue in sparse grid refinement near boundaries. Corrected…
Corrected exception management within greedy iterations. Removed unnecessary…
Added eigenvalue examples.
Modified initial sample pruning to return parameterList.
Increased robustness of division by rational denominator. Added numpy linalg…
Removed unused partial dual energy norm matrix. Fixed last-iteration plotting…
Removed forced subsampling in generating parameters over shapes. Now samples…
Module for the solution and rational model order reduction of parametric PDE-based problem. Coded in Python 3.6.
- numpy and scipy;
- fenics and mshr;
- and other standard Python3 modules (os, typing, time, datetime, abc, pickle, traceback, and itertools).
Most of the high fidelity problem engines already provided rely on FEniCS. If you do not have FEniCS installed, you may want to create an Anaconda3/Miniconda3 environment using the provided conda-fenics.yml environment file by running the command
conda env create --file conda-fenics.yml
This will create an environment where Fenics (and all other required modules) can be used. In order to use FEniCS, the environment must be activated through
source activate fenicsenv
Clone the repository
git clone https://c4science.ch/source/RROMPy.git
enter the main folder and install the package by typing
python3 setup.py install
The installation can be tested with
python3 setup.py test
This project is licensed under the GNU GENERAL PUBLIC LICENSE license - see the LICENSE file for details.
Part of the funding that made this module possible has been provided by the Swiss National Science Foundation through the FNS Research Project No. 182236.