Python library for Rational Reduced Order Modeling
Bumped version to 1.9
Removed useless error estimators. Made sample points for interpolatory…
Updated MOR engines and tests to account for changes in total vandermonde and…
Simplified total vandermonde matrix generation with automatic column reordering.
Improved affinity check for HFEngines. Added option for max greedy error…
Added checks for existence of method for building affine blocks of HFEngine.
Added centered-like method (quite unstable). TODO: decide whether to keep it.
Improved robustness of trainedModel reset.
Fixed missing imports in marginal engine. Fixed bulk sample management in…
Fixed missing import. Increased robustness of greedy error estimator following…
Fixed incompatibility between sampleList and tensordot.
Fixed hanging comma.
Added robustness check for interpolatory residual estimator of imported MOR…
Fixed hfengine spacedim bugs. Fixed access to C in MOR engines.
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.