Welcome to pyLOM’s documentation#
Version: 3.1.0
Useful links: Source Repository | Issue Tracker | Wiki
pyLOM is a high-performance-computing enabled tool for massively parallel reduced order modelling. This tool includes parallel algorithms for the proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and spectral proper orthogonal decomposition (SPOD) as well as a set of deep learning architectures for model order reduction and surrogate modelling such as variational autoencoders. All pyLOM modules are GPU-enabled.
Getting started
See some examples of how to use pyLOM. The examples are short and easy to understand, and they will help you get started with the library.
Installation guide
To install the tool you can see the instructions in the wiki. The tool is available in PyPI and can be installed using pip.
API reference
The reference guide contains a detailed description of the functions, modules, and objects included in pyLOM. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.
Contributor’s guide
Want to add to the codebase? The contributing guidelines will guide you through the process of improving pyLOM.