Error Estimation in the Mean‐Field Limit of Kinetic Flocking Models With Local Alignments

ABSTRACT In this paper, we present an innovative particle system characterized by moderate interactions, designed to accurately approximate kinetic flocking models that incorporate singular interaction forces and local alignment mechanisms. We establish the existence of weak solutions to the corresponding flocking equations and provide an error estimate for the mean‐field limit. This is achieved through the regularization of singular forces and a nonlocal approximation strategy for local alignments. We show that, by selecting the regularization and localization parameters logarithmically with respect to the number of particles, the particle system effectively approximates the mean‐field equation.

Authors

Institutions

Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2025-10-09
DOI
https://doi.org/10.1002/mma.70201
Primary Topic
Advanced Thermodynamics and Statistical Mechanics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Error Estimation in the Mean‐Field Limit of Kinetic Flocking Models With Local Alignments

Hui Huang, Jinhuan Wang, Keyu Li
Mathematical Methods in the Applied Sciences
Advanced Thermodynamics and Statistical Mechanics
article

Error Estimation in the Mean‐Field Limit of Kinetic Flocking Models With Local Alignments

Hui Huang, Jinhuan Wang, Keyu Li
article en

Abstract

ABSTRACT In this paper, we present an innovative particle system characterized by moderate interactions, designed to accurately approximate kinetic flocking models that incorporate singular interaction forces and local alignment mechanisms. We establish the existence of weak solutions to the corresponding flocking equations and provide an error estimate for the mean‐field limit. This is achieved through the regularization of singular forces and a nonlocal approximation strategy for local alignments. We show that, by selecting the regularization and localization parameters logarithmically with respect to the number of particles, the particle system effectively approximates the mean‐field equation.

Mathematical Methods in the Applied SciencesVol. 49(2)
Liaoning University (CN), Hunan University (CN)
Openalex Percentile: Top 78%
Advanced Thermodynamics and Statistical Mechanics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.