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-0.5.10.tar.gz
(130.0 kB
view details)
Built Distribution
opinf-0.5.10-py3-none-any.whl
(158.1 kB
view details)
File details
Details for the file opinf-0.5.10.tar.gz.
File metadata
- Download URL: opinf-0.5.10.tar.gz
- Upload date:
- Size: 130.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 1afd8d905b12e3370086f1150b241235555070df60d46b506226a1b69674c121 |
|
| MD5 | e7739a0082566b5455c1ae4837737848 |
|
| BLAKE2b-256 | 09b326500b72f36cc2b2f2cb2c8c7a6a1e65421382db7a95e04bbbad3d4fd371 |
File details
Details for the file opinf-0.5.10-py3-none-any.whl.
File metadata
- Download URL: opinf-0.5.10-py3-none-any.whl
- Upload date:
- Size: 158.1 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 | 1e097972fa4d48f3fd75dc1c952a05c4ccd07d3882b30f16b91cd05d1319b406 |
|
| MD5 | b46f47a331254d2499e63e17a34f499d |
|
| BLAKE2b-256 | 55a8c4a2b447527ef42417f0a7873c1d63776ee1a495208b1209fe0d493f649f |
