Neural Network-Aided Triple Decomposition of 3D Flow

Description

Tools for training neural networks for the purpose of predicting residual vorticity (residual strain and shear as well) of a velocity gradient.

Installation

Pip

# clone project
git clone https://github.com/danherbriley/flow-analysis
cd your-repo-name

# [OPTIONAL] create conda environment
python3.10 -m venv venv
source venv/bin/activate

# install pytorch according to instructions
# https://pytorch.org/get-started/

# install requirements
pip install -r requirements.txt

Conda

# clone project
git clone https://github.com/danherbriley/flow-analysis
cd your-repo-name

# create conda environment and install dependencies
conda env create -f environment.yaml -n myenv

# activate conda environment
conda activate myenv

How to run

Train model with default configuration

# train on CPU
python src/train.py trainer=cpu

# train on GPU
python src/train.py trainer=gpu

Train model with chosen experiment configuration from configs/experiment/

python src/train.py experiment=experiment_name.yaml

You can override any parameter from command line like this

python src/train.py trainer.max_epochs=20 data.batch_size=64

Notes

• Seed for reproducibility is set globally in src/train.py