UQpy: A general purpose Python package and development environment for uncertainty quantification

Olivier, Audrey; Giovanis, Dimitris G.; Aakash, B. S.; Chauhan, Mohit; Vandanapu, Lohit; Shields, Michael D.

doi:10.1016/j.jocs.2020.101204

Title: UQpy: A general purpose Python package and development environment for uncertainty quantification

Journal Article · Tue Sep 15 00:00:00 EDT 2020 · Journal of Computational Science

DOI:https://doi.org/10.1016/j.jocs.2020.101204· OSTI ID:1853642

Olivier, Audrey ^[1]; Giovanis, Dimitris G. ^[1]; Aakash, B. S. ^[1]; Chauhan, Mohit ^[1]; Vandanapu, Lohit ^[1]; Shields, Michael D. ^[1]

Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Civil and Systems Engineering

In this paper, we present the UQpy software toolbox, an open-source Python package for general uncertainty quantification (UQ) in mathematical and physical systems. The software serves as both a user-ready toolbox that includes many of the latest methods for UQ in computational modeling and a convenient development environment for Python programmers advancing the field of UQ. The paper presents an introduction to the software's architecture and existing capabilities, divided in the code in a set of modules centered around different UQ tasks such as sampling methods, generation of random processes and random fields, probabilistic inverse modeling, reliability analysis, surrogate modeling, and active learning. The paper also highlights the importance of the RunModel module, which is used to drive simulations in the uncertainty analyses performed in UQpy. This module conveniently allows the user to define computational models directly in Python, or to run simulations from a third-party software in serial or in parallel. To illustrate the various capabilities, two examples are tracked throughout the paper and analyzed repeatedly for various UQ tasks. The first is a Python model solving a nonlinear structural dynamics problem, used to illustrate UQpy's capabilities in sampling and forward propagation of high dimensional random vectors (stochastic processes), and probabilistic inference. The second model is a third-party Abaqus finite element model solving the thermomechanical response of a beam structure. This example is used to illustrate UQpy's capabilities in variance reduction sampling techniques, reliability analysis, surrogate modeling and active learning techniques.

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Research Organization:: Johns Hopkins Univ., Baltimore, MD (United States)

Sponsoring Organization:: USDOE Office of Science (SC); US Department of the Navy, Office of Naval Research (ONR); National Science Foundation (NSF); US Army Research Office (ARO)

Grant/Contract Number:: SC0020428; N00014-15-1-2754; N00014-16-1-2582; N00014-18-1-2644; 1652044; W911NF-12-2- 0022

OSTI ID:: 1853642

Alternate ID(s):: OSTI ID: 1661709

Journal Information:: Journal of Computational Science, Vol. 47, Issue C; ISSN 1877-7503

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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