Operator Inference for data-driven model reduction of dynamical systems.
Project description
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.
Contributors: Shane McQuarrie, Renee Swischuk, Elizabeth Qian, Boris Kramer, Karen Willcox.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
opinf-0.5.11.tar.gz
(130.6 kB
view details)
Built Distribution
opinf-0.5.11-py3-none-any.whl
(158.6 kB
view details)
File details
Details for the file opinf-0.5.11.tar.gz.
File metadata
- Download URL: opinf-0.5.11.tar.gz
- Upload date:
- Size: 130.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 7f1b63f0ca7a7987a27f425b4704765406619f6681a3f8991a14d1b15f71b5d1 |
|
| MD5 | a1427dadf1b2c109c1420d55d5b10e29 |
|
| BLAKE2b-256 | 0539ef716142feebbc159900174332a21e5320a6ac3b80f379b0f2d15a484b04 |
File details
Details for the file opinf-0.5.11-py3-none-any.whl.
File metadata
- Download URL: opinf-0.5.11-py3-none-any.whl
- Upload date:
- Size: 158.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 6819b5c1b8b2f41406da80b9f37ba7474d5c12cc289e1a30c1068987e5db4aee |
|
| MD5 | a2a04f719b770b1a06c6adacbfe4e13e |
|
| BLAKE2b-256 | aa2c1afb30f33fc0d10929c605ddb148f666c2246048609a142b2a10e04917d4 |
