Message-Passing Monte Carlo: Generating low-discrepancy point sets via graph neural networks.

ABSTRACT: Discrepancy is a well-known measure for the irregularity of the distribution of a point set. Point sets with small discrepancy are called low discrepancy and are known to efficiently fill the space in a uniform manner. Low-discrepancy points play a central role in many problems in science and engineering, including numerical integration, computer vision, machine perception, computer graphics, machine learning, and simulation. In this work, we present a machine learning approach to generate a new class of low-discrepancy point sets named Message-Passing Monte Carlo (MPMC) points. Motivated by the geometric nature of generating low-discrepancy point sets, we leverage tools from Geometric Deep Learning and base our model on graph neural networks. We further provide an extension of our framewo

SUBMITTER: Rusch TK 

PROVIDER: S-EPMC11459188 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature