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Hodge_Laplacian_GNN: PDE-Constrained Hodge–Laplacian Graph Neural Network Framework

Domaine:

healthcare

Type de record:

softwaremodel
Créateur:
Eno
Éditeur:
Zenodo
Hôte:avatar

This repository contains the implementation of a PDE-constrained Hodge–Laplacian Graph Neural Network framework for operator-level modeling of biological transport dynamics.

The framework combines:
- Discrete exterior calculus (DEC)
- Hodge decomposition
- PDE-constrained graph learning
- Advection–diffusion transport modeling
- Coexact/exact operator decomposition
- Physics-informed biological graph inference

The repository includes:
- Core training pipelines
- Benchmarking utilities
- Baseline comparison workflows
- Spatial biological operator analysis
- Reproducibility scripts

Applications include:
- Tumor–immune interface analysis
- Non-passive transport detection
- Operator-based biological dynamics modeling
- Mechanistic graph representation learning

Author: Anas Enoch, MD
Affiliation: Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco

Visit

doi.org

Languages

Ndasa

Tags

graph neural networkshodge laplaciandiscrete exterior calculuscomputational biologysystems biologyphysics informed machine learninggeometric deep learningbiological transportpde constrained learningspatial transcriptomics+2

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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