pswpswpsw/nif
A library for dimensionality reduction on spatial-temporal PDE
This project helps engineers and scientists create simplified models of complex physical systems that evolve over space and time. It takes raw simulation data from these systems (like fluid dynamics or heat transfer) and produces a reduced-order model. This model can then be used for faster predictions, optimization, or to understand the system's behavior with fewer computational resources. It's designed for those working with large-scale spatial-temporal dynamics, especially in fields like computational fluid dynamics or structural analysis.
Use this if you need to perform dimensionality reduction on parametric spatial-temporal fields, especially from simulations with complex or adaptive meshes, and you want to avoid common limitations of traditional methods like lossy interpolation.
Not ideal if your data is not spatial-temporal or if you are not dealing with large-scale physics simulations that require reduced-order modeling.
Stars
73
Forks
15
Language
Jupyter Notebook
License
LGPL-2.1
Category
Last pushed
Dec 28, 2025
Commits (30d)
0
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