Working space for a research project exploring super-resolution of 2D turbulence.
The project was created with:
- python 3.9.13
- NumPy
- SciPy
- jax 0.4.2
- jax-cfd 0.2.0
- fenics-dolfin 2019.1.0
- Oasis 2018
After the conda environment is created, e.g.,
conda create -n observer python=3.9
we then install the required packages. For data generation, we are using JAX-CFD. For the application to work, JAX needs to be installed. A base install with pip install JAX-CFD only requires NumPy, SciPy and JAX.
To install a CPU-only version of JAX, which might be useful for doing local development on a laptop, you can run
pip install --upgrade pip
pip install --upgrade "jax[cpu]"
If you want to install JAX with NVIDIA GPU support, you can use CUDA and CUDNN installed from pip wheels; CUDA and CuDNN installation need to match the wheels available, e.g.,
pip install --upgrade pip
# Installs the wheel compatible with CUDA 11 and cuDNN 8.6 or newer.
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda11_local]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
More details are available in the JAX documentation.
To locally install JAX-CFD, do the following:
git clone https://github.com/google/jax-cfd.git
cd jax-cfd
pip install jaxlib
pip install -e ".[complete]"
To install FEniCS follow the instructions in the documentation.
Oasis is installed with regular distutils
git clone https://github.com/mikaem/Oasis.git
cd Oasis
python setup.py install --prefix='Path to where you want Oasis installed. Must be on PYTHONPATH'
We are assuming that the installation is in the current work directory, i.e., './'.
To be able to run the CFD solver, we need Oasis and a compatible installation of FEniCS. After downloading Oasis, apply a patch provided to a file in the Oasis folder:
patch -u ./Oasis/oasis/NSfracStep.py -i NSfracStep.patch
If not present, create two empty folders: data and observer_results.
The following steps will enable data generation. We simulate 2D Kolmogorov flow for a given set of parameters. Executing:
mkdir data/r123_g256by256_nu0.006
python kolmogorov_flow.py 123 512 0.006 data/r123_g256by256_nu0.006
will produce simulation data that were used in our study with the grid resolution of 512x512, viscosity set to 0.006 and 123 being a random number used for the initial field construction. The results will be stored in the defined folder data/r123_g256by256_nu0.006.
You can also just take advantage of the script for multiple case generation generate_observations.sh. Specify settings inside and execute:
./generate_observations.sh
We also provide a script NoiseForwardTemplate_mpi.py which enables data generation using FEniCS.
To run the experiments with the observer, use the script provided to set up a number of different cases:
./generate_cases_and_run_study.sh
Inside the script, provide the parameters for the study:
WORK_DIR=$PWD/observer_results
NPROCS=2
grids=( 64)
crs=( 1 2 4 8 16)
alphas=( 0.2 0.4 0.8)
gains=( 5)
seeds=($(seq 123 1 123))
nu=0.006
You can specify the number of processors for MPI run, the grid size, compression ratios (crs), the amount of noise added (alphas), viscosity, and the gain of the observer.
If this script is run on a MAC, add '' to the lines with sed, i.e., replace
sed -i "s/gridflag/$g/" NoiseLuenberger_mpi.py
with
sed -i '' "s/gridflag/$g/" NoiseLuenberger_mpi.py
Otherwise, remove.