| Name | opinf JSON |
| Version |
0.5.11
JSON |
| download |
| home_page | None |
| Summary | Operator Inference for data-driven model reduction of dynamical systems. |
| upload_time | 2024-11-20 21:51:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2023 Willcox Research Group Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
operator inference
model reduction
data-driven model reduction
scientific machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
[](./LICENSE)
[](https://www.python.org)
[](https://black.readthedocs.io/en/stable/)
[](https://github.com/Willcox-Research-Group/rom-operator-inference-python3/issues)
[](https://github.com/Willcox-Research-Group/rom-operator-inference-python3/commits/main)
[](https://pypi.org/project/opinf/)
[](https://Willcox-Research-Group.github.io/rom-operator-inference-Python3/)
# 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/).
---
**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/).
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