Metadata-Version: 2.1
Name: UQit
Version: 1.0.0
Summary: A Python Package for Uncertainty Quantification (UQ) in Computational Fluid Dynamics (CFD)
Home-page: https://github.com/KTH-Nek5000/UQit
Author: Saleh Rezaeiravesh
Author-email: salehr@kth.se
License: UNKNOWN
Description: ![`UQit`](./docs/source/_static/uqit_logo.png?style=centerme)
        ## A Python Package for Uncertainty Quantification (UQ) in Computational Fluid Dynamics (CFD)
        SimEx/FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden <br/>
        #
        
        ### Features:
        * **Sampling**:
          - Various stochastic and spectral types of samples
        
        * **Uncertainty propagation or UQ forward problem**: 
          - generalized Polynomial Chaos Expansion (gPCE)
          - Probabilistic PCE (PPCE)
        
        * **Global sensitivity analysis (GSA)**:
          - Sobol sensitivity indices
        
        * **Surrogates**:
          - Lagrange interpolation
          - gPCE
          - Gaussian process regression (GPR) 
        
        ### Installation:
        `pip install UQit`
        
        ### Build documentation:
        First, you need [`Sphinx`](https://www.sphinx-doc.org/en/master/) to be installed: 
        * `conda install sphinx`
        * `conda install -c conda-forge nbsphinx`
        
        Then,
        * `cd docs`
        * `make html`
        
        Open `index.html` in `docs/build/html/`
        
        ### Required libraries:
         * General  
           - [`numpy`](https://numpy.org/)
           - [`scipy`](https://www.scipy.org/)
           - [`matplotlib`](https://matplotlib.org/)
         * Optional
           - [`cvxpy`](https://www.cvxpy.org/) (for compressed sensing in PCE)
           - [`PyTorch`](https://pytorch.org/) (for GPR)
           - [`GPyTorch`](https://gpytorch.ai/) (for GPR)
        
        ### Bugs/Questions
        * In case there is a bug, please feel free to open an issue on Github. 
        
        * Qestions/comments:
          - Saleh Rezaeiravesh, salehr@kth.se <br/>
          - Philipp Schlatter, pschlatt@mech.kth.se 
        
        ### Publications related to UQit:
        * [Rezaeiravesh S., Vinuesa R., Schlatter P., An Uncertainty-Quantification Framework for Assessing Accuracy, Sensitivity, and Robustness in Computational Fluid Dynamics, arXiv:2007.07071, 2020.](https://arxiv.org/abs/2007.07071)
        
        ## Release Notes
        ### Release 1, 10.10.2020
        Source code, documentation, tests and notebooks are provided for the above-listed features. 
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
