fpichi/gca-rom
GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.
This project helps engineers and scientists analyze complex physical phenomena much faster by reducing the complexity of their computational models. It takes high-fidelity simulation data, often from Partial Differential Equations (PDEs), and produces a simplified, yet accurate, representation. This allows researchers and computational scientists to quickly explore different scenarios or parameters without needing to run full-scale, time-consuming simulations every time.
Use this if you need to accelerate the analysis of large-scale computational fluid dynamics, structural mechanics, or other physics-based simulations.
Not ideal if your models are already simple enough that the computational cost is not a significant bottleneck, or if you primarily work with discrete, non-spatial data.
Stars
37
Forks
11
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Nov 13, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/fpichi/gca-rom"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
a-r-j/graphein
Protein Graph Library
raamana/graynet
Subject-wise networks from structural MRI, both vertex- and voxel-wise features (thickness, GM...
pykale/pykale
Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for...
dmlc/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.