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Detail publikačního výsledku
TSAPETIS, D.; SHIELDS, M.; GIOVANIS, D.; OLIVIER, A.; NOVÁK, L.; CHAKROBORTY, P.; SHARMA, H.; CHAUHAN, M.; KONTOLATI, K.; VANDANAPU, L.; LOUKREZIS, D.; GARDNER, M.
Originální název
UQpy v4.1: Uncertainty quantification with Python
Anglický název
Druh
Článek WoS
Originální abstrakt
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.
Anglický abstrakt
Klíčová slova
Uncertainty quantification
Klíčová slova v angličtině
Autoři
Rok RIV
2024
Vydáno
12.12.2023
Nakladatel
Elsevier
ISSN
2352-7110
Periodikum
SoftwareX
Svazek
24
Číslo
1
Stát
Nizozemsko
Strany od
Strany do
7
Strany počet
URL
https://www.sciencedirect.com/science/article/pii/S2352711023002571?ref=cra_js_challenge&fr=RR-1
BibTex
@article{BUT187079, author="TSAPETIS, D. and SHIELDS, M. and GIOVANIS, D. and OLIVIER, A. and NOVÁK, L. and CHAKROBORTY, P. and SHARMA, H. and CHAUHAN, M. and KONTOLATI, K. and VANDANAPU, L. and LOUKREZIS, D. and GARDNER, M.", title="UQpy v4.1: Uncertainty quantification with Python", journal="SoftwareX", year="2023", volume="24", number="1", pages="1--7", doi="10.1016/j.softx.2023.101561", issn="2352-7110", url="https://www.sciencedirect.com/science/article/pii/S2352711023002571?ref=cra_js_challenge&fr=RR-1" }