discrete-heat-kernels-simplicial
CommunitySmooth higher-order signals on simplicial complexes.
Data & Analytics#higher-order#brain-networks#simplicial#heat-kernel#hodge-laplacian#topological-signal-processing
Authorhiyenwong
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Discrete heat kernel smoothing on simplicial complexes extends classical signal processing to higher-dimensional structures, enabling denoising and analysis of signals on k-simplices beyond vertices and edges, with potential applications in functional brain networks.
Core Features & Use Cases
- Simplicial complex construction and allocation of higher-order structures
- Hodge Laplacian based diffusion for smoothing signals on k-simplices
- Boundary operators and efficient sparse computation
- Applications to higher-order network analysis and brain connectivity
Quick Start
Load your simplicial dataset and apply heat kernel smoothing on a chosen k to begin processing.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
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Please help me install this Skill: Name: discrete-heat-kernels-simplicial Download link: https://github.com/hiyenwong/ai_collection/archive/main.zip#discrete-heat-kernels-simplicial Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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