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-Loeve 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

  • Bert Debusschere

  • Emilie Grace Baillo

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.