A diagram-free approach to the stochastic estimates in regularity structures

Fuente: arXiv
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Autores principales: Linares, Pablo, Otto, Felix, Tempelmayr, Markus, Tsatsoulis, Pavlos
Formato: Preprint
Publicado: 2021
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author Linares, Pablo
Otto, Felix
Tempelmayr, Markus
Tsatsoulis, Pavlos
author_facet Linares, Pablo
Otto, Felix
Tempelmayr, Markus
Tsatsoulis, Pavlos
contents In this paper, we explore the version of Hairer's regularity structures based on a greedier index set than trees, as introduced by Otto, Sauer, Smith and Weber. More precisely, we construct and stochastically estimate the renormalized model avoiding the use of Feynman diagrams but still in a fully automated, i. e. inductive way. This is carried out for a class of quasi-linear parabolic PDEs driven by noise in the full singular but renormalizable range. We assume a spectral gap inequality on the (not necessarily Gaussian) noise ensemble. The resulting control on the variance of the model naturally complements its vanishing expectation arising from the BPHZ-choice of renormalization. We capture the gain in regularity on the level of the Malliavin derivative of the model by describing it as a modelled distribution. Symmetry is an important guiding principle and built-in on the level of the renormalization Ansatz. Our approach is analytic and top-down rather than combinatorial and bottom-up.
format Preprint
id arxiv_https___arxiv_org_abs_2112_10739
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle A diagram-free approach to the stochastic estimates in regularity structures
Linares, Pablo
Otto, Felix
Tempelmayr, Markus
Tsatsoulis, Pavlos
Probability
Analysis of PDEs
60H17, 60L30, 60H07, 81T16
In this paper, we explore the version of Hairer's regularity structures based on a greedier index set than trees, as introduced by Otto, Sauer, Smith and Weber. More precisely, we construct and stochastically estimate the renormalized model avoiding the use of Feynman diagrams but still in a fully automated, i. e. inductive way. This is carried out for a class of quasi-linear parabolic PDEs driven by noise in the full singular but renormalizable range. We assume a spectral gap inequality on the (not necessarily Gaussian) noise ensemble. The resulting control on the variance of the model naturally complements its vanishing expectation arising from the BPHZ-choice of renormalization. We capture the gain in regularity on the level of the Malliavin derivative of the model by describing it as a modelled distribution. Symmetry is an important guiding principle and built-in on the level of the renormalization Ansatz. Our approach is analytic and top-down rather than combinatorial and bottom-up.
title A diagram-free approach to the stochastic estimates in regularity structures
topic Probability
Analysis of PDEs
60H17, 60L30, 60H07, 81T16
url https://arxiv.org/abs/2112.10739