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Mean-Field Limits for Stochastic Interacting Particles on Digraph Measures

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arxiv 2403.20325 v1 pith:J665JGJW submitted 2024-03-29 math.AP math.PR

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keywords mean-fieldstochasticapproachescapturedgmsinteractinglimitsmeasures
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Many natural phenomena are effectively described by interacting particle systems, which can be modeled using either deterministic or stochastic differential equations (SDEs). In this study, we specifically investigate particle systems modeled by SDEs, wherein the mean field limit converges to a Vlasov-Fokker-Planck-type equation. Departing from conventional approaches in stochastic analysis, we explore the network connectivity between particles using diagraph measures (DGMs). DGMs are one possible tool to capture sparse, intermediate and dense network/graph interactions in the mean-field thereby going beyond more classical approaches such as graphons. Since the main goal is to capture large classes of mean-field limits, we set up our approach using measure-theoretic arguments and combine them with suitable moment estimates to ensure approximation results for the mean-field.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mean-field limits \`a la Tanaka and large deviations for particle systems with network interactions

    math.PR 2025-10 conditional novelty 6.0 of 10

    For non-exchangeable particle systems with network interactions, the paper proves mean-field limits and a new large-deviation principle for the interaction measure, with a relative-entropy rate function, under Lipschi...

  2. A Dynamical Systems Perspective on the Analysis of Neural Networks

    math.DS 2025-07 conditional novelty 3.0 of 10

    A survey of how dynamical systems theory can rigorously analyze neural networks, presenting theorems on expressivity, training stability, and mean-field limits mostly from the authors' own preprints.

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