This paper presents the UQpy software toolbox, an open-source Python package for general uncertainty quantification (UQ) in mathematical and physical systems. The software serves as both a user-ready toolbox that includes many of the latest methods for UQ in computational modeling and a convenient development environment for Python programmers advancing the field of UQ. The paper presents an introduction to the software's architecture and existing capabilities, divided in the code in a set of modules centered around different UQ tasks such as sampling methods, generation of random processes and random fields, probabilistic inverse modeling, reliability analysis, surrogate modeling, and active learning. The paper also highlights the importance of the RunModel module, which is used to drive simulations in the uncertainty analyses performed in UQpy. This module conveniently allows the user to define computational models directly in Python, or to run simulations from a third-party software in serial or in parallel. To illustrate the various capabilities, two examples are tracked throughout the paper and analyzed repeatedly for various UQ tasks. The first is a Python model solving a nonlinear structural dynamics problem, used to illustrate UQpy's capabilities in sampling and forward propagation of high dimensional random vectors (stochastic processes), and probabilistic inference. The second model is a third-party Abaqus finite element model solving the thermomechanical response of a beam structure. This example is used to illustrate UQpy's capabilities in variance reduction sampling techniques, reliability analysis, surrogate modeling and active learning techniques.

本文介绍了UQpy软件工具箱，这是一个开放源代码Python软件包，用于数学和物理系统中的一般不确定性量化（UQ）。该软件既可以用作用户就绪的工具箱，其中包括许多用于计算建模的UQ的最新方法，也可以作为为推进UQ领域的Python程序员提供便利的开发环境。本文介绍了该软件的体系结构和现有功能，并在代码中将模块划分为围绕不同UQ任务的一组模块，这些模块包括采样方法，随机过程和随机字段的生成，概率逆建模，可靠性分析，替代建模，和积极学习。本文还强调了RunModel的重要性模块，用于驱动在UQpy中执行的不确定性分析中的仿真。该模块方便用户使用Python直接定义计算模型，或以串行或并行方式从第三方软件运行仿真。为了说明各种功能，本文跟踪了两个示例，并针对各种UQ任务进行了重复分析。第一个是解决非线性结构动力学问题的Python模型，用于说明UQpy在采样和正向传播高维随机矢量（随机过程）以及概率推断方面的能力。第二个模型是解决梁结构热力学响应的第三方Abaqus有限元模型。这个例子用来说明UQpy在方差减少采样技术，可靠性分析，替代建模和主动学习技术方面的能力。