This project is designed for a wind tunnel testing experiment. It focuses on:
Surrogate Modeling: Building and evaluating surrogate models based on wind tunnel data.
Optimization: Determining optimal configurations for wind tunnel experiments and sensor selection while maintaining efficiency and accuracy.
conda create -n ML_wind_tunnel_testing python=3.11
conda activate ML_wind_tunnel_testing
pip install -r requirements.txt
pre-commit install
Ensure you have the data file ./data/raw/ML_test.xlsx and run
python examples
Reports will be written in ./reports/ML_test.xlsx
For detailed information about the algorithms and optimization techniques used in this project, refer to the Documentation.
├── LICENSE <- Open-source license if one is chosen
├── README.md <- The top-level README for developers using this project.
│
├── config
│ └── config.toml <- Model/project config params.
│
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default mkdocs project; see mkdocs.org for details
│
├── pyproject.toml <- Project configuration file with package metadata for agentme
│ and configuration for tools like black
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.cfg <- Configuration file for flake8
│
├── examples
│ └── wind_tunnel_testing <- Running examples of all functionality.
│
├── tests
│ ├── test_wind_tunnel_testing <- Unit tests for modeling methods.
│ └── conftest.py <- test config.
│
└── wind_tunnel_testing <- Source code for use in this project.
│
├── __init__.py <- Makes wind_tunnel_testing a Python module
│
├── config.py <- Script to define overall paths and config params
│
├── dataset.py <- Scripts to download or generate data
│
├── features.py <- Scripts to turn raw data into features for modeling
│
└── modeling.py <- Scripts to train models and then use trained models to make
predictions