This paper describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to “enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.” This paper provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package.

本文介绍了 PyOED，这是一个高度可扩展的科学包，能够开发和测试逆问题的模型约束最优实验设计 (OED)。具体来说，PyOED 旨在成为模型约束 OED的综合Python 工具包。该软件包面向有兴趣了解《牛津英语词典》公式和方法细节的科学家和研究人员。它还旨在使研究人员能够通过各种测试问题（例如模拟模型）来试验标准和创新的 OED 技术。 OED、反演问题（例如贝叶斯反演）和数据同化（DA）是密切相关的研究领域，并且它们的表述显着重叠。因此，PyOED 不断通过大量贝叶斯反演、DA 和 OED 方法以及新的科学模拟模型、观测误差模型和观测算子进行扩展。添加这些部分，以便可以对它们进行排列，以便能够在不同复杂性的各种设置中测试 OED 方法。 PyOED 核心完全用 Python 编写，并利用了固有的面向对象功能；然而，当前版本的 PyOED 旨在可扩展而不是可伸缩。具体来说，PyOED 的开发目的是“以最少的编码工作实现 OED 方法的快速开发和基准测试，并最大限度地提高代码重用率。”本文简要描述了 PyOED 的布局和理念，并提供了一组示例性测试用例和教程来展示该包的潜力。