SURG collectively develops the Uncertainty Quantification with Python (UQpy) software. UQpy is a general python toolkit and development environment for uncertainty quantification. UQpy has grown to feature 14 modules with state-of-the-art capabilities. The code is open-source and operates under an MIT license. You can find it on Github here and it is extensively documented here.
UQpy has been designed in a convenient, object-oriented and modularized architecture that makes it readily extensible. We welcome contributions and provide guidance for contributors on the Github page.
The development team has included numerous students and postdocs who have been lead at various times by lead developers Dimitris Giovanis, Audrey Olivier, Dimitris Tsapetis, and Connor Krill. Additionally, several contributions have been made from outside of SURG and from researchers across the world.
For additional references, UQpy has been published in the following journal articles, with a third publication dedicated to the newly established Scientific Machine Learning module currently in review.
- D. Tsapetis et al. “UQpy v4.1: Uncertainty Quantification with Python”. SoftwareX (2023). [DOI]
- A. Olivier, B.S. Aakash, M. Chauhan, L. Vandanapu, D.G. Giovanis, and M. D. Shields. “UQpy: A general purpose Python package and development environment for uncertainty quantification”. Journal of Computational Science. 47 (2020), p. 101204. [DOI]