SROMPy 0.0.1

Stochastic Reduced Order Models with Python

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:

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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!

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Author:
James Warner
UQ Center of Excellence
NASA Langley Research Center
[e-mail]

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