Python library for Rational Reduced Order Modeling
Diffusion RationalROMPy (master)
Recent Commits
Recent Commits
| Commit | Author | Details | Committed | ||||
|---|---|---|---|---|---|---|---|
| 6643b38fd8ae | pradovera | Version 1.8 | Sep 6 | ||||
| a9cb6ac43925 | pradovera | Bumped version to 1.8 | Sep 6 | ||||
| d2f3921cc61d | pradovera | Improved stability of dot and solve operations for tensorized sampling. | Sep 6 | ||||
| fef77d0cf8c2 | pradovera | Replaced allowRepeatedSamples with uniqueness check for sample points. | Sep 6 | ||||
| 03970578f5b0 | pradovera | Removed batch sampling from examples. | Aug 30 | ||||
| ba6c30fad025 | pradovera | Added tensorized sample management (dot and solve). Removed batch sampling. | Aug 30 | ||||
| c8d735333cac | pradovera | Added nearest neighbor. Fixed instability management in rational greedy. | Aug 30 | ||||
| eae301846e1f | pradovera | Improved greedy a posteriori estimation, removed broken estimators, added EIM… | Aug 22 | ||||
| f4ed36217e2b | pradovera | Improved hfengine blocks assembly. Added recompression of homogeneized RHS. | Aug 21 | ||||
| 3ed8cef19cfc | pradovera | Updated examples and tests to symbolic affine dependence. Added first 2d greedy… | Aug 21 | ||||
| 03dd8e8e1854 | pradovera | Reorganized hfengines using symbolic expressions. Modified MOR methods… | Aug 21 | ||||
| b3be051c499c | pradovera | Added symbolic expression support. Moved degree and derivative support to… | Aug 21 | ||||
| c4288be2f200 | pradovera | Fixed discrepancy-based residual estimator for all polynomial bases. | Aug 16 | ||||
| 64b0ade86c90 | pradovera | Added support for discrepancy-based residual estimator. Only works for monomial… | Aug 16 | ||||
| a289b48cebd8 | pradovera | Unified sample generation template. Modified engines, tests, and samples… | Aug 13 |
README.md
README.md
RROMPy -- Rational Reduced Order Modeling in Python
Module for the solution and rational model order reduction of parametric PDE-based problem. Coded in Python 3.6.
Prerequisites
RROMPy requires
- numpy and scipy;
- fenics and mshr;
- matplotlib;
- and other standard Python3 modules (os, typing, time, datetime, abc, pickle, traceback, and itertools).
Testing requires
- pytest.
Fenics
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
Installing
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
License
This project is licensed under the GNU GENERAL PUBLIC LICENSE license - see the LICENSE file for details.
Acknowledgments
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.
c4science · Help