Abstract
In a recent landmark paper, Khoa Lê (Electron. J. Probab. 25 (2020) 38) established a stochastic sewing lemma which since has found many applications in stochastic analysis. He further conjectured that a similar result may hold in the context of the reconstruction theorem within Hairer’s regularity structures. The purpose of this article is to provide such a stochastic reconstruction theorem. We also discuss two variations of this theorem, motivated by different constructions of stochastic integration against white noise. Our formulation makes use of the distributional viewpoint of Caravenna–Zambotti (EMS Surv. Math. Sci. 7 (2020) 207–251).
Dans un récent article important, Khoa Lê (Electron. J. Probab. 25 (2020) 38) a établi un lemme de couture stochastique qui a depuis trouvé de nombreuses applications en analyse stochastique. Khoa Lê a également émis l’hypothèse qu’un résultat similaire pourrait s’appliquer dans le contexte du théorème de reconstruction au sein des structures de régularité d’Hairer. Le but de cet article est de fournir un tel théorème de reconstruction stochastique. Nous discutons également deux variations de ce théorème, motivées par différentes constructions d’intégration stochastique pour un bruit blanc en espace-temps. Notre formulation utilise le point de vue distributionnel de Caravenna–Zambotti (EMS Surv. Math. Sci. 7 (2020) 207–251).
Funding Statement
This project was supported by the IRTG 2544, which is funded by the DFG.
Acknowledgements
I want to thank Peter Friz for suggesting this topic to me, and for many helpful discussions throughout the process of writing this paper. I further want to thank Khoa Lê and Philipp Forstner for their helpful remarks, and Carlo Bellingeri for our mathematical discussions. Last but not least, I want to thank the anonymous reviewers whose feedback greatly improved the quality of this paper.
Citation
Hannes Lutz Kern. "A stochastic reconstruction theorem." Ann. Inst. H. Poincaré Probab. Statist. 60 (4) 2468 - 2507, November 2024. https://doi.org/10.1214/23-AIHP1407
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