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Uncertainty Quantification using Polynomial Chaos Expansion (UQPCE) is an open-source, python-based research code for use in parametric, non-deterministic computational studies. UQPCE utilizes a non-intrusive polynomial chaos expansion surrogate modeling technique to efficiently estimate uncertainties for computational analyses.

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UQPCE

Uncertainty Quantification with Polynomial Chaos Expansion (UQPCE) is an open-source, python-based research code for use in parametric, non-deterministic computational studies. UQPCE utilizes a non-intrusive polynomial chaos for computational analyses. The software allows the user to perform an automated uncertainty analysis for any given computational code without requiring modification to the source. UQPCE estimates sensitivities, confidence intervals, and other model statistics, which can be useful in the conceptual design and analysis of flight vehicles. This software was developed for the Aeronautics Systems Analysis Branch within the Systems Analysis and Concepts Directorate at NASA Langley Research Center.

Documentation

Documentation for the most recent verison of UQPCE can be found here.

Installation

Install library with pip install . in the location of your choice.

Run the unittests using python -m unittest discover uqpce

Notices

Copyright © 2020-2024 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved.

The NASA UQPCE Software (LAR-20514-1) calls the following third-party software, which is subject to the terms and conditions of its licensor, as applicable at the time of licensing. The third-party software is not bundled or included with this software but may be available from the licensor. License hyperlinks are provided here for information purposes only.

Python Copyright © 2001-2024 Python Software Foundation; All Rights Reserved PSF license: https://docs.python.org/3/license.html

SymPy Copyright (c) 2006-2023 SymPy Development Team Modified BSD license: https://github.com/sympy/sympy/blob/master/LICENSE

SciPy Copyright (c) 2001-2002 Enthought, Inc. 2003-2024, SciPy Developers. All rights reserved. Modified BSD license: https://github.com/scipy/scipy/blob/main/LICENSE.txt

NumPy Copyright (c) 2005-2024, NumPy Developers. All rights reserved. Modified BSD license: https://github.com/numpy/numpy/blob/main/LICENSE.txt

MatPlotLib Copyright (c) 2012- Matplotlib Development Team; All Rights Reserved PSF license: https://matplotlib.org/stable/users/project/license.html

MPI4Py Copyright (c) 2013, Lisandro Dalcin. All rights reserved. BSD license: https://github.com/erdc/mpi4py/blob/master/LICENSE.txt

pyYAML Copyright (c) 2017-2021 Ingy döt Net Copyright (c) 2006-2016 Kirill Simonov MIT license: https://github.com/yaml/pyyaml/blob/main/LICENSE

openMDAO Apache License, Version 2.0: https://github.com/OpenMDAO/OpenMDAO/blob/master/LICENSE.txt

Disclaimers

No Warranty: THE SUBJECT SOFTWARE IS PROVIDED "AS IS" WITHOUT ANY WARRANTY OF ANY KIND, EITHER EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR FREEDOM FROM INFRINGEMENT, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL BE ERROR FREE, OR ANY WARRANTY THAT DOCUMENTATION, IF PROVIDED, WILL CONFORM TO THE SUBJECT SOFTWARE. THIS AGREEMENT DOES NOT, IN ANY MANNER, CONSTITUTE AN ENDORSEMENT BY GOVERNMENT AGENCY OR ANY PRIOR RECIPIENT OF ANY RESULTS, RESULTING DESIGNS, HARDWARE, SOFTWARE PRODUCTS OR ANY OTHER APPLICATIONS RESULTING FROM USE OF THE SUBJECT SOFTWARE. FURTHER, GOVERNMENT AGENCY DISCLAIMS ALL WARRANTIES AND LIABILITIES REGARDING THIRD-PARTY SOFTWARE, IF PRESENT IN THE ORIGINAL SOFTWARE, AND DISTRIBUTES IT "AS IS."

Waiver and Indemnity

RECIPIENT AGREES TO WAIVE ANY AND ALL CLAIMS AGAINST THE UNITED STATES GOVERNMENT, ITS CONTRACTORS AND SUBCONTRACTORS, AS WELL AS ANY PRIOR RECIPIENT. IF RECIPIENT'S USE OF THE SUBJECT SOFTWARE RESULTS IN ANY LIABILITIES, DEMANDS, DAMAGES, EXPENSES OR LOSSES ARISING FROM SUCH USE, INCLUDING ANY DAMAGES FROM PRODUCTS BASED ON, OR RESULTING FROM, RECIPIENT'S USE OF THE SUBJECT SOFTWARE, RECIPIENT SHALL INDEMNIFY AND HOLD HARMLESS THE UNITED STATES GOVERNMENT, ITS CONTRACTORS, AND SUBCONTRACTORS, AS WELL AS ANY PRIOR RECIPIENT, TO THE EXTENT PERMITTED BY LAW. RECIPIENT'S SOLE REMEDY FOR ANY SUCH MATTER SHALL BE THE IMMEDIATE, UNILATERAL TERMINATION OF THIS AGREEMENT.

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Uncertainty Quantification using Polynomial Chaos Expansion (UQPCE) is an open-source, python-based research code for use in parametric, non-deterministic computational studies. UQPCE utilizes a non-intrusive polynomial chaos expansion surrogate modeling technique to efficiently estimate uncertainties for computational analyses.

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