There are 1 repository under manifolds topic.
⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.
🏔️Manopt. jl – Optimization on Manifolds in Julia
Source code for lecture notes
Distance-based Analysis of DAta-manifolds in python
Python implementation of the paper "Discrete Differential-Geometry Operators for Triangulated 2-Manifolds" by Meyer et. al. VisMath 2002
Comprehensive open source book on basic topology, smooth manifolds, differential geometry, Lie theory, homological algebra, and index theory.
Tangent bundle, vector space and Submanifold definition
Tensor algebra abstract type interoperability setup
Supplementary code for the paper "Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces"
A package to describe amortized (conditional) normalizing-flow PDFs defined jointly on tensor products of manifolds with coverage control. The connection between different manifolds is fixed via an autoregressive structure.
Methods for computational information geometry
Differential equations on manifolds
This repository contains the python implementation of the paper titled "Discrete Differential-Geometry Operators for Triangulated 2-Manifolds" by Meyer et. al. VisMath 2002 http://multires.caltech.edu/pubs/diffGeoOps.pdf
This is a Pytorch implementation of [normalizing flows on tori and spheres, ICML 2020]
This packaged is an implementation of our paper "Robust Denoising of Piece-Wise Smooth Manifolds", ICASSP 2018 The algorithm creates an affinity graph and perform denoising on a set of N input points in R^n. Given an input set of points in any arbitrary dimension, an affinity graph is first created based on Tensor Voting, Local PCA or Euclidean distances, or the Tensor Voting Graph [3] . Then it performs denoising using a modified version of the recently proposed MFD algorithm[1]. The MFD algorithm uses the Spectral Graph Wavelet (SGW) transform in order to perform denoising directly in the spectral graph wavelet domain. Main function - Main_Demo provides an example of running our algorithm
Adaptive P/ODE numerics with Grassmann element TensorField assembly
Differentiation on manifolds
Slepian Scale-Discretised Wavelets in Python
Maurer-Cartan-Lie frame connections ∇ Grassmann.jl TensorField derivations
Materiales del Curso Aprendizaje Geométrico Profundo, Posgrado Matemáticas UNAM 2023-1
Computing homology groups of simplicial complexes
🏔️⛷️ ManoptExamples.jl – A collection of research and tutorial example problems for Manopt.jl
Extensive learning notes on mathematics
Solutions and clarifications for Tensor Calculus by J.L. Synge and A. Schild (Dover Publication)
Learning-Rate-Free Stochastic Riemannian Optimization in JAX.
An autoregressive, quaternion manifold model for rapidly estimating complex SO(3) distributions.
Normal distributions on manifolds
Riemmanian Manifold representation library with automatic first order differentiation