This repository includes an implementation of the Polynomial Chaos Expansion method.
More comprehensive tools on the same subject are available (e.g. Chaospy), this repository is born during a self-learning activity of the authors.
At the moment, one can use this module to study the uncertainty propagation of a model with uncertain inputs. The following aspects are implemented:
- each uncertain variable can be associated to a uniform or normal distribution
- evaluation of the coefficient with spectral projection method
- global sensitivity analysis with Sobol' indices
If you use this module you can consider to cite the following paper direct link.
Giaccone, L.; Lazzeroni, P.; Repetto, M. Uncertainty Quantification in Energy Management Procedures. Electronics 2020, 9, 1471. https://doi.org/10.3390/electronics9091471
In this paper the pce module has been used successfully to estimate uncertainties. You can also find all codes associated to the paper here https://github.com/giaccone/cogen_eval.
The project is developed using Python 3. The installer requires a Python version >= 3.6.
Other requirements (I tend to use always the latest version of the following libraries):
- numpy
- scipy
- matplotlib
- joblib
This project is deployed through the Python Package Index, therefore, it can be easily obtained by running the following command:
pip install pce