A limit theory for controlled McKean–Vlasov SPDEs

We develop a limit theory for controlled mean field stochastic partial differential equations in a variational framework. More precisely, we prove existence results for mean field limits and particle approximations, and we establish a set-valued propagation of chaos result which shows that sets of empirical distributions converge to sets of mean field limits in the Hausdorff metric topology. Further, we discuss limit theorems related to stochastic optimal control theory. To illustrate our findings, we apply them to a controlled interacting particle system of stochastic porous media equations.

Authors

Publication Details

Journal
Journal of Differential Equations
Published
2026-07-17
DOI
https://doi.org/10.1016/j.jde.2026.114618
Citations
1
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
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article

A limit theory for controlled McKean–Vlasov SPDEs

David Criens
1 citations
Journal of Differential Equations
Markov Chains and Monte Carlo Methods
article

A limit theory for controlled McKean–Vlasov SPDEs

David Criens
article en
1 citations

Abstract

We develop a limit theory for controlled mean field stochastic partial differential equations in a variational framework. More precisely, we prove existence results for mean field limits and particle approximations, and we establish a set-valued propagation of chaos result which shows that sets of empirical distributions converge to sets of mean field limits in the Hausdorff metric topology. Further, we discuss limit theorems related to stochastic optimal control theory. To illustrate our findings, we apply them to a controlled interacting particle system of stochastic porous media equations.

Journal of Differential EquationsVol. 479
Openalex Percentile: Top 99%
Markov Chains and Monte Carlo Methods
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A limit theory for controlled McKean–Vlasov SPDEs — David Criens · Journal of Differential Equations (2026) | TGRS Research Map | TGRS