Python library for Rational Reduced Order Modeling
Diffusion RationalROMPy (master)
Recent Commits
Recent Commits
| Commit | Author | Details | Committed | ||||
|---|---|---|---|---|---|---|---|
| 9945053422d9 | pradovera | Version 2.8.1 | Sep 30 | ||||
| 7c878f5d7758 | pradovera | Bumped version to 2.8.1 | Sep 30 | ||||
| fb546aae4e7e | pradovera | Bugfix of greedy postprocessing and chordal metric computation. | Sep 30 | ||||
| 8381b3a71177 | pradovera | Version 2.8 | Sep 29 | ||||
| 3f646196dac6 | pradovera | Bumped version to 2.8 | Sep 29 | ||||
| ff557502f6c8 | pradovera | Improved alternative methods for computing 1D rational approximant (added… | Sep 29 | ||||
| 068522db0163 | pradovera | Hotfix for removed parameter in pivoted approximants. | Sep 25 | ||||
| 5c2ab7641888 | pradovera | Improved folder structure and code commenting. | Sep 25 | ||||
| 2924ba713efc | pradovera | Added hybrid POD option for column normalization and related sampling engine. | Aug 27 | ||||
| f1d7f17aaa9b | pradovera | Added 2-way sparse grid refinement. | Aug 21 | ||||
| 490f86110f73 | pradovera | Fixed bug with preemptive end of greedy loop in pivoted approximation. Made… | Aug 18 | ||||
| f168c4c11054 | pradovera | Made match pivoted greedy rely on hat functions within outer loop. Removed… | Aug 17 | ||||
| 8ec0029bfa45 | pradovera | Added absolute cutOff tolerances for poles. Added proxys for mu0 and… | Aug 5 | ||||
| 3daebf5d46ea | pradovera | Turned residueTol and cutOffTolerances into properties of HFEngine in… | Jul 30 | ||||
| 18c23959ac9b | pradovera | Improved piecewise linear interpolation using triangular structure of… | Jul 30 |
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.
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 command
conda create -n fenicsenv -c conda-forge pytest scipy matplotlib fenics mshr
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
conda activate fenicsenv
See the Anaconda documentation for more information.
Installing
Clone the repository
git clone http://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 182236.
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