REVIEW 2 cited by
Mean-Field Limits for Stochastic Interacting Particles on Digraph Measures
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
Mean-field limits \`a la Tanaka and large deviations for particle systems with network interactions
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...
-
A Dynamical Systems Perspective on the Analysis of Neural Networks
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.
Discussion (0). Continue with ORCID to comment.