Metadata-Version: 2.1
Name: opinf
Version: 0.4.1
Summary: Operator inference for data-driven, non-intrusive model reduction of dynamical systems.
Home-page: https://github.com/Willcox-Research-Group/rom-operator-inference-Python3
Author: Willcox Research Group
Author-email: kwillcox@oden.utexas.edu
Maintainer: Shane A. McQuarrie
Maintainer-email: shanemcq@utexas.edu
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: h5py (>=2.9.0)
Requires-Dist: numpy (>=1.16)
Requires-Dist: scipy (>=1.3)
Requires-Dist: matplotlib (>=3.0)
Requires-Dist: scikit-learn (>=0.18)

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# Operator Inference in Python

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](https://www.sciencedirect.com/science/article/pii/S0045782516301104).

[**See the Documentation Here**](https://Willcox-Research-Group.github.io/rom-operator-inference-Python3/).

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**Contributors**:
[Shane McQuarrie](https://github.com/shanemcq18),
[Renee Swischuk](https://github.com/swischuk),
[Elizabeth Qian](https://github.com/elizqian),
[Boris Kramer](http://kramer.ucsd.edu/),
[Karen Willcox](https://kiwi.oden.utexas.edu/).
