Computing on Topological Domains
-
Updated
May 24, 2024 - Jupyter Notebook
Computing on Topological Domains
This Python code generates a Combinatorial Discrete Cell Complex for the slip planes arrangement in FCC and BCC lattices as a set of Adjacency and Incidence matrices
We introduce simplicial VAR models for time-varying processes observed on higher-order networks. These models adeptly capture intricate spatio-temporal dependencies within simplicial signals while significantly reducing parameter complexity relative to conventional VAR models.
Representation Learning on Topological Domains
ggplot2 extension to visualize persistent homology
Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics
A toolkit for discrete calculus.
Python bindings to Simplex Tree data structure (w/ C++)
Package documentation
Geometries for 3D rendering, including normals, UVs and cell indices (faces). Perfect if you want to supercharge your dependency folder... with 30KB of geometries.
Multidimensional interpolation using simplicial complexes.
A general form for complex data
R package for simplifying general computation on simplicial complexes
Creating/visualizing simplicial complexes out of randomly generated data with computing some filterations
Tool to numerically solve and analyse simplicial Kuramoto models.
Repository for "Invariant Representations of Embedded Simplicial Complexes"
Inductive Higher Order Knowledge Hypergraph Completion
R package porting Ripser-based persistent homology calculation engines from C++ via Rcpp. Currently ports Ripser (Vietoris-Rips complex) and Cubical Ripser (cubical complex).
Add a description, image, and links to the simplicial-complex topic page so that developers can more easily learn about it.
To associate your repository with the simplicial-complex topic, visit your repo's landing page and select "manage topics."