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Stochastic Reduced Order Models with Python

Project description

SROMPy - Stochastic Reduced Order Models with Python

Python module for generating Stochastic Reduced Order Models (SROMs) and applying them for uncertainty quantification problems. See documentation in docs/ directory for details.

Example usage:

from postprocess import Postprocessor
from srom import SROM
from target import NormalRandomVariable

#Initialize Normal random variable object to be modeled by SROM:
normal = NormalRandomVariable(mean=3., std_dev=1.5)

#Initialize SROM & optimize to model the normal random variable:
srom = SROM(size=10, dim=1)
srom.optimize(normal)

#Compare the CDF of the SROM & target normal variable:
pp = Postprocessor(srom, normal)
pp.compare_CDFs()

The above code snippet produces the following CDF comparison plot:

CDF comparison


If you use SROMPy for your research, please cite the technical report:

Warner, J. E. (2018). Stochastic reduced order models with Python (SROMPy). NASA/TM-2018-219824.

The report can be found in the docs/references directory. Thanks!


Author:
James Warner
UQ Center of Excellence
NASA Langley Research Center
james.e.warner@nasa.gov


Notices: Copyright 2018 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. No copyright is claimed in the United States under Title 17, U.S. Code. All Other Rights Reserved.

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