Helper functions for NSE as LPV with Neural Networks
Project description
Neural Networks for NSE as low-dimensional LPV
Active workflow
Installation
# package for sparse cholesky factorizations
# not needed but speed up with FEM norms and POD
apt install libsuitesparse-dev
pip install scikit-sparse==0.4.5
# fenics -- for the FEM part
apt install fenics # see https://fenicsproject.org/download/
# install this module and helper modules
pip install .
Generate the data
cd ../simulations-training-data
mkdir simu-data
mkdir train-data
source start-generic-tdp-sim.sh
# python3 time_dep_nse_generic.py
cd -
Check the NN
python3 data_fem_checks.py
python3 CNN_AE.py
Python Machine-Learning Resources
- an overview
- Tensorflow -- see below
- Pytorch -- see below
- NeuroLab
- ffnet
- Scikit-Neural Network
- Lasagne
- pyrenn
Tensorflow
- website
- Article for understanding NN using TensorFlow
- Previous experience with building NN
- Visualization feature TensorBoard
- Based on keras
PyTorch
- website
- Tutorial for simple NN
- Recommended by colleagues (Lessig, Richter)
Scikit-Learn
- website
- looks well maintained
- many routines for data processing
- a few on neural network
Install
# not needed anymore
# pip install -e . # Python3 needed here! install the module (and one dependency)
pip install -e
installs the module nse_nn_lpv
to be used in the tests/...
but keeps track of all changes made in nse_nn_lpv
.
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