PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments

TL;DR

PyOED is an extensible Python toolkit designed for developing, testing, and benchmarking model-constrained optimal experimental design methods in inverse problems, data assimilation, and Bayesian inversion.

Contribution

The paper introduces PyOED, a comprehensive, extensible Python package that facilitates rapid development and testing of OED methods with diverse models and approaches.

Findings

PyOED supports a wide range of OED, DA, and Bayesian inversion methods.

The package enables testing in various complex settings with minimal coding.

Exemplary test cases demonstrate its versatility and ease of use.

Abstract

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.