ArnauMiro/pyLowOrder
High performance parallel reduced order Modelling library
This tool helps computational scientists and engineers analyze complex fluid dynamics or other high-dimensional simulation data more efficiently. It takes large datasets from simulations and condenses them into simplified models using techniques like Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD). The output is a reduced model that captures the essential behavior, allowing for faster analysis and prediction by researchers working with computational fluid dynamics or similar fields.
Available on PyPI.
Use this if you need to extract dominant patterns and create simplified representations from large-scale, high-fidelity simulation data, especially when working with fluid dynamics.
Not ideal if your data is not from simulations or if you don't need to perform dimensionality reduction for complex physical systems.
Stars
27
Forks
8
Language
Python
License
MIT
Category
Last pushed
Mar 20, 2026
Commits (30d)
0
Dependencies
6
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ArnauMiro/pyLowOrder"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
pdebench/PDEBench
PDEBench: An Extensive Benchmark for Scientific Machine Learning
tum-pbs/PhiFlow
A differentiable PDE solving framework for machine learning
peterdsharpe/NeuralFoil
NeuralFoil is a practical airfoil aerodynamics analysis tool using physics-informed machine...
lettucecfd/lettuce
Computational Fluid Dynamics based on PyTorch and the Lattice Boltzmann Method
EMSL-Computing/Pore2Chip
A python package that takes XCT images of porous materials and generates representative digital...