PyOED:用于数据同化和模型约束优化实验设计的可扩展套件

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Abhijit Chowdhary, Shady E. Ahmed, Ahmed Attia
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引用次数: 0

摘要

本文介绍了 PyOED,这是一个高度可扩展的科学软件包,可用于开发和测试逆问题的模型约束优化实验设计(OED)。具体来说,PyOED 的目标是成为模型约束最优实验设计(OED)的综合性 Python 工具包。该工具包面向有兴趣了解 OED 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利用各种测试问题(如仿真模型)尝试标准和创新的 OED 技术。OED、反演问题(如贝叶斯反演)和数据同化(DA)是密切相关的研究领域,它们的公式有很大的重叠。因此,PyOED 正在通过大量的贝叶斯反演、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不断扩展。添加的这些组件可以进行排列组合,以便在各种复杂环境中测试 OED 方法。PyOED 的核心完全用 Python 编写,并利用了 Python 本身面向对象的能力;不过,PyOED 当前版本的目的是可扩展,而不是可升级。具体来说,PyOED 的开发目的是 "以最小的编码工作量实现 OED 方法的快速开发和基准测试,并最大限度地提高代码的再利用率"。本文简要介绍了 PyOED 的布局和理念,并提供了一组示例测试用例和教程,以展示该软件包的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments

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.

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来源期刊
ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software 工程技术-计算机:软件工程
CiteScore
5.00
自引率
3.70%
发文量
50
审稿时长
>12 weeks
期刊介绍: As a scientific journal, ACM Transactions on Mathematical Software (TOMS) documents the theoretical underpinnings of numeric, symbolic, algebraic, and geometric computing applications. It focuses on analysis and construction of algorithms and programs, and the interaction of programs and architecture. Algorithms documented in TOMS are available as the Collected Algorithms of the ACM at calgo.acm.org.
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