Statistical conservation laws for scalar model problems: Hierarchical evolution equations

Fuente: arXiv
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Autores principales: Huang, Qian, Rohde, Christian
Formato: Preprint
Publicado: 2025
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author Huang, Qian
Rohde, Christian
author_facet Huang, Qian
Rohde, Christian
contents The probability density functions (PDFs) for the solution of the incompressible Navier-Stokes equation can be represented by a hierarchy of linear equations. This article develops new hierarchical evolution equations for PDFs of a scalar conservation law with random initial data as a model problem. Two frameworks are developed, including multi-point PDFs and single-point higher-order derivative PDFs. These hierarchies capture statistical correlations and guide closure strategies.
format Preprint
id arxiv_https___arxiv_org_abs_2508_15359
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Statistical conservation laws for scalar model problems: Hierarchical evolution equations
Huang, Qian
Rohde, Christian
Analysis of PDEs
Numerical Analysis
The probability density functions (PDFs) for the solution of the incompressible Navier-Stokes equation can be represented by a hierarchy of linear equations. This article develops new hierarchical evolution equations for PDFs of a scalar conservation law with random initial data as a model problem. Two frameworks are developed, including multi-point PDFs and single-point higher-order derivative PDFs. These hierarchies capture statistical correlations and guide closure strategies.
title Statistical conservation laws for scalar model problems: Hierarchical evolution equations
topic Analysis of PDEs
Numerical Analysis
url https://arxiv.org/abs/2508.15359