UQpy Version 4.2: Uncertainty quantification with Python
We introduce a new module for the UQpy software package which extends its capabilities into the field of Scientific Machine Learning. This module builds on PyTorch to create a flexible and robust platform for uncertainty quantification in machine learning. The scientific machine learning module of UQpy introduces custom layers, neural networks, and neural network trainers that are compatible with torch version 2.2.2 and allow for “plug and play” integration into existing torch code.
Authors
- Dimitris G. Giovanis (ORCID: https://orcid.org/0000-0003-2272-2584)
- George D. Pasparakis (ORCID: https://orcid.org/0000-0001-7352-9106)
- Somdatta Goswami (ORCID: https://orcid.org/0000-0002-8255-9080)
- Ponkrshnan Thiagarajan (ORCID: https://orcid.org/0000-0003-3946-3902)
- Michael D. Shields (ORCID: https://orcid.org/0000-0003-1370-6785)
- Dimitrios Tsapetis (ORCID: https://orcid.org/0000-0002-4339-1035)
- Connor Krill
Institutions
- Johns Hopkins University (US)
Publication Details
- Journal
- SoftwareX
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1016/j.softx.2025.102364
- Primary Topic
- Nuclear Engineering Thermal-Hydraulics
- Type
- article
- Field-Weighted Citation Impact
- 0.00