About

Overview

The Python Toolkit for Uncertainty Quantification (PyTUQ) is a Python-only set of tools designed for uncertainty quantification. Key PyTUQ capabilities include, but are not limited to:

  • Methods for Gaussian process regression

  • Global sensitivity analysis methods

  • SVD-based dimensionality reduction techniques

  • Karhunen-Loève expansions

  • Various methods for linear regression

  • Bayesian compressive sensing techniques

  • MCMC classes for calibration and parameter inference

  • Classes and transformations for multivariate random variables

  • Neural network and polynomial chaos expansion surrogate model construction

  • Utilities including decorators, test functions, integration classes, and workflows

Authors

  • Khachik Sargsyan (Sandia National Laboratories)

  • Bert Debusschere (Sandia National Laboratories)

  • Emilie Grace Baillo (Sandia National Laboratories)

Contributors

  • Habib N. Najm (Sandia National Laboratories)

  • Javier Murgoitio-Esandi (Google)

  • Cosmin Safta (Sandia National Laboratories)

  • Joy Bahr-Mueller (Sandia National Laboratories)

  • Vahan Sargsyan (Stuyvesant High School)

Acknowledgements

This work is supported by the Scientific Discovery through Advanced Computing (SciDAC) Program under the Office of Science at the U.S. Department of Energy.

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.