This is a Python implementation of Operator Inference for learning projection-based polynomial reduced-order models of dynamical systems. The procedure is data-driven and non-intrusive, making it a viable candidate for model reduction of "glass-box" systems. The methodology was introduced in 2016 by Peherstorfer and Willcox.
Contributors: Shane McQuarrie, Renee Swischuk, Elizabeth Qian, Boris Kramer, Karen Willcox.
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Operator Inference for data-driven model reduction of dynamical systems.
We found that opinf demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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