A simple algorithm for uniform sampling on the surface of a hypersphere

04/05/2022
by   Stefan Schnabel, et al.
0

We propose a simple method for uniform sampling of points on the surface of a hypersphere in arbitrarily many dimensions. By avoiding the evaluation of computationally expensive functions like logarithms, sines, cosines, or higher order roots the new method is faster than alternative techniques.

READ FULL TEXT
research
01/21/2023

Spatial Attention Kinetic Networks with E(n)-Equivariance

Neural networks that are equivariant to rotations, translations, reflect...
research
04/04/2023

Sampling from the surface of a curved torus: A new genesis

The distributions of toroidal data, often viewed as an extension of circ...
research
03/20/2021

Learning Continuous Cost-to-Go Functions for Non-holonomic Systems

This paper presents a supervised learning method to generate continuous ...
research
04/23/2020

Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficients

Simulating atmospheric turbulence is an essential task for evaluating tu...
research
02/21/2016

Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques

In a series of recent works, we have generalised the consistency results...
research
12/19/2018

Coroutines with Higher Order Functions

Coroutines are non-preemptive concurrent subroutines that, unlike preemp...
research
05/12/2019

HLO: Half-kernel Laplacian Operator for Surface Smoothing

This paper presents a simple yet effective method for feature-preserving...

Please sign up or login with your details

Forgot password? Click here to reset