UQpy v4.1: Uncertainty Quantification with Python
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Abstract (arXiv)
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 continous 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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 May 2023.
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Citing papers per year: 2023: 2; 2024: 5; 2025: 3.
10 papers.
- 2025Neural Chaos: A Spectral Stochastic Neural Operatorcited by 21
- 2024Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantificationcited by 17
- 2023Physics-Informed Polynomial Chaos Expansionscited by 15
- 2025Polynomial Chaos Expansion for Operator Learningcited by 7
- 2024Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantificationcited by 6
- 2023Learning thermodynamically constrained equations of state with uncertaintycited by 5
- 2024On Fractional Moment Estimation from Polynomial Chaos Expansioncited by 4
- 2024Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networkscited by 2
- 2024A Resolution Independent Neural Operatorcited by 0
- 2025Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Samplingcited by 0
- 2025Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Sampling11 Dec 2025
- 2025Polynomial Chaos Expansion for Operator Learning28 Aug 2025
- 2025Neural Chaos: A Spectral Stochastic Neural Operator17 Feb 2025
- 2024A Resolution Independent Neural Operator17 Jul 2024
- 2024On Fractional Moment Estimation from Polynomial Chaos Expansion4 Mar 2024
- 2024Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification23 Feb 2024
- 2024Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification30 Jan 2024
- 2024Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks10 Jan 2024
- 2023Physics-Informed Polynomial Chaos Expansions4 Sep 2023
- 2023Learning thermodynamically constrained equations of state with uncertainty29 Jun 2023