What is it about?
PyOED is a Python-based package that brings together variational and Bayesian data assimilation algorithms for inverse problems, optimal design of experiments, and novel optimization and reinforcement learning solvers, into an integrated extensible research environment.
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Why is it important?
PyOED aims to balance between providing advanced methodologies, relying on minimal backends or packages, and being easy-to-use for non-experts who wish to develop and test new ideas for Bayesian inversion and/or optimal design of experiments. PyOED is developed as open-source, requests and contributions are welcome.
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This page is a summary of: PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments, ACM Transactions on Mathematical Software, March 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3653071.
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