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:
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
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