UQpy v4.1: Uncertainty quantification with Python

Dimitrios Tsapetis; Michael D. Shields; Dimitris G. Giovanis; Audrey Olivier; Lukas Novak; Promit Chakroborty; Himanshu Sharma; Mohit Chauhan; Katiana Kontolati; Lohit Vandanapu; Dimitrios Loukrezis; Michael Gardner

SoftwareX (Dec 2023)

UQpy v4.1: Uncertainty quantification with Python

Dimitrios Tsapetis,
Michael D. Shields,
Dimitris G. Giovanis,
Audrey Olivier,
Lukas Novak,
Promit Chakroborty,
Himanshu Sharma,
Mohit Chauhan,
Katiana Kontolati,
Lohit Vandanapu,
Dimitrios Loukrezis,
Michael Gardner

Affiliations

Dimitrios Tsapetis: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
Michael D. Shields: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Corresponding author.
Dimitris G. Giovanis: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
Audrey Olivier: Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, 90089, USA
Lukas Novak: Faculty of Civil Engineering, Brno University of Technology, Veveri 331/95, Brno 60200, Czech Republic
Promit Chakroborty: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
Himanshu Sharma: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
Mohit Chauhan: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
Katiana Kontolati: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
Lohit Vandanapu: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
Dimitrios Loukrezis: Department of Electrical Engineering and Information Technology, Technical University of Darmstadt, Darmstadt, Germany
Michael Gardner: Department of Civil and Environmental Engineering, University of California, Davis, CA, 95616, USA

Journal volume & issue: Vol. 24
p. 101561

Abstract

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This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to simplify previous tightly coupled features, and improve its extensibility and modularity. To improve the robustness of UQpy, software engineering best practices were adopted. A new software development workflow significantly improved collaboration between team members, and continuous integration and automated testing ensured the robustness and reliability of software performance. Continuous deployment of UQpy allowed its automated packaging and distribution in system agnostic format via multiple channels, while a Docker image enables the use of the toolbox regardless of operating system limitations.

Published in SoftwareX

ISSN: 2352-7110 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
Website: http://www.journals.elsevier.com/softwarex/

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