LTesan/HybridGraphNets
This GitHub repository hosts a integration of biomecanical thermodynamics with graph neural networks (GNNs) to power a cutting-edge hepatic digital twin.
This project helps biomedical engineers and researchers simulate and predict how liver tissue deforms and responds over time under various forces, building a "digital twin" of a hepatic system. It takes in data from simulations of liver tissue mechanics (like position, velocity, and stress components across different geometries and meshes) and outputs predictions of how the tissue will change dynamically, even for new, untrained geometries. This is ideal for those involved in computational biology, biophysics, or precision medicine aiming to understand complex biological material behavior.
Use this if you need to accurately predict the time-dependent mechanical behavior of viscoelastic biological tissues, particularly the liver, using a robust model that can generalize to new geometries.
Not ideal if your primary focus is on static structural analysis, or if you do not have access to simulation data of tissue mechanics for training.
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8
Forks
1
Language
Python
License
AGPL-3.0
Category
Last pushed
Mar 10, 2026
Commits (30d)
0
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