PyOED#

PyOED brings together variational and Bayesian data assimilation algorithms for inverse problems, optimal design of experiments, and novel optimization, statistical, and reinforcement learning solvers, into an integrated extensible research environment.

Tip

If you are new to PyOED, we recomended to:

first experiment with the step-by-step guidance/examples, then
inspect PyOED’s configurations approach.
After that, the user can follow our list of examples (scripts) and Tutorials (Jupyter Notebooks).
The full documentation can then give guidance on all details of PyOED.

Working with PyOED#

Install

Getting Started
- Use Existing PyOED classes

Examples
- Tutorials (Jupyter Notebooks)
- Advanced Examples (Python Scripts)

User Guide

Citing PyOED#

Tip

If you are utilizing PyOED in your research, please cite PyOED’s paper and repository.

Bibtex: pyoed_citation.bib
Chicago:
1. Chowdhary, Abhijit, Shady E. Ahmed, and Ahmed Attia. “PyOED: An extensible suite for data assimilation and model-constrained optimal design of experiments.” ACM Transactions on Mathematical Software 50, 2 (2024), https://doi.org/10.1145/3653071.
2. 1. Attia, PyOED: An extensible suite for data assimilation and model-constrained optimal design of experiments, (2024), ahmedattia/pyoed.

PyOED Structure at a Glance#