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- W4380136729 abstract "We present two different arguments using stochastic analysis to construct super-renormalizable tensor field theories, namely the $mathrm{T}^4_3$ and $mathrm{T}^4_4$ models. The first approach is the construction of a Langevin dynamic combined with a PDE energy estimate while the second is an application of the variational approach of Barashkov and Gubinelli. By leveraging the melonic structure of divergences, regularising properties of non-local products, and controlling certain random operators, we demonstrate that for tensor field theories these arguments can be significantly simplified in comparison to what is required for $Phi^4_d$ models." @default.
- W4380136729 created "2023-06-10" @default.
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- W4380136729 date "2023-06-08" @default.
- W4380136729 modified "2023-10-18" @default.
- W4380136729 title "A Stochastic Analysis Approach to Tensor Field Theories" @default.
- W4380136729 doi "https://doi.org/10.48550/arxiv.2306.05305" @default.
- W4380136729 hasPublicationYear "2023" @default.
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