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Extended decorations on naturally decorated trees were introduced in the work of Bruned, Hairer and Zambotti on algebraic renormalization of regularity structures to provide a convenient framework for the renormalization of systems of…

Probability · Mathematics 2021-02-03 Ismael Bailleul , Yvain Bruned

We give a survey of recent result regarding scaling limits of systems from statistical mechanics, as well as the universality of the behaviour of such systems in so-called cross-over regimes. It transpires that some of these universal…

Probability · Mathematics 2018-03-09 Martin Hairer

In this work we prove convergence of renormalised models in the framework of regularity structures [Hai14] for a wide class of variable coefficient singular SPDEs in their full subcritical regimes. In particular, we provide for the first…

Analysis of PDEs · Mathematics 2025-07-10 Lucas Broux , Harprit Singh , Rhys Steele

We give a systematic description of a canonical renormalisation procedure of stochastic PDEs containing nonlinearities involving generalised functions. This theory is based on the construction of a new class of regularity structures which…

Rings and Algebras · Mathematics 2018-11-20 Yvain Bruned , Martin Hairer , Lorenzo Zambotti

High-precision predictions in BSM models require calculations at the loop-level and thus a renormalization of (some of) the BSM parameter. Here many choices for the renormalization scheme (RS) are possible. A given RS can be well suited to…

High Energy Physics - Phenomenology · Physics 2023-04-26 S. Heinemeyer , F. von der Pahlen

We construct a procedure for Bogoliubov-Parasiuk-Hepp-Zimmermann (BPHZ) renormalization of a rough path in view of the relation between rough path theory and regularity structure. We also provide a plain expression of the BPHZ-renormalized…

Probability · Mathematics 2021-03-15 Hayahide Ito

The formalism recently introduced in arXiv:1610.08468 allows one to assign a regularity structure, as well as a corresponding "renormalisation group", to any subcritical system of semilinear stochastic PDEs. Under very mild additional…

Analysis of PDEs · Mathematics 2021-05-24 Yvain Bruned , Ajay Chandra , Ilya Chevyrev , Martin Hairer

We prove a general theorem on the stochastic convergence of appropriately renormalized models arising from nonlinear stochastic PDEs. The theory of regularity structures gives a fairly automated framework for studying these problems but…

Probability · Mathematics 2018-01-23 Ajay Chandra , Martin Hairer

We provide an algebraic framework to describe renormalization in regularity structures based on multi-indices for a large class of semi-linear stochastic PDEs. This framework is ``top-down", in the sense that we postulate the form of the…

Probability · Mathematics 2024-09-04 Yvain Bruned , Pablo Linares

This work deals with singular stochastic PDEs driven by non-translation invariant differential operators. We describe the renormalized equation for a very large class of spacetime dependent renormalization schemes. Our approach bypasses in…

Analysis of PDEs · Mathematics 2024-06-05 I. Bailleul , Y. Bruned

We present a novel framework for the study of a large class of non-linear stochastic PDEs, which is inspired by the algebraic approach to quantum field theory. The main merit is that, by realizing random fields within a suitable algebra of…

Mathematical Physics · Physics 2021-11-12 Claudio Dappiaggi , Nicolò Drago , Paolo Rinaldi , Lorenzo Zambotti

The explorations of models beyond the Standard Model (BSM) naturally involve scans over the unknown BSM parameters. On the other hand, high precision predictions require calculations at the loop-level and thus a renormalization of (some of)…

High Energy Physics - Phenomenology · Physics 2024-07-01 S. Heinemeyer , F. von der Pahlen

We consider the variance renormalisation of a singular SPDE for which a Da Prato-Debussche trick is not applicable. The example taken is the $2$-dimensional generalised parabolic Anderson model (gPAM), driven by a much rougher than white…

Probability · Mathematics 2026-02-20 Máté Gerencsér , Yueh-Sheng Hsu

We develop a renormalization theory of non-perturbative dissipative H\'enon-like maps with combinatorics of bounded type. The main novelty of our approach is the incorporation of Pesin theoretic ideas to the renormalization method, which…

Dynamical Systems · Mathematics 2024-11-14 Jonguk Yang

We construct renormalised models of regularity structures by using a recursive formulation for the structure group and for the renormalisation group. This construction covers all the examples of singular SPDEs which have been treated so far…

Probability · Mathematics 2023-10-24 Yvain Bruned

We give a proof of the convergence of the BHZ renormalized model associated with the generalized (KPZ) equation that does not require the full strength of the BPHZ renormalisation. Our approach is based on a convenient form of chaos…

Probability · Mathematics 2026-01-27 I. Bailleul , Y. Bruned

Normal multi-scale transform [4] is a nonlinear multi-scale transform for representing geometric objects that has been recently investigated [1, 7, 10]. The restrictive role of the exact order of polynomial reproduction $P_e$ of the…

Numerical Analysis · Mathematics 2013-11-19 Stanislav Harizanov

Non-perturbative renormalisation of a general class of scalar field theories is performed at the Hartree level truncation of the 2PI effective action in the broken symmetry regime. Renormalised equations are explicitly constructed for the…

High Energy Physics - Phenomenology · Physics 2008-11-26 G. Fejos , A. Patkos , Zs. Szep

We start in this work the study of the relation between the theory of regularity structures and paracontrolled calculus. We give a paracontrolled representation of the reconstruction operator and provide a natural parametrization of the…

Analysis of PDEs · Mathematics 2019-10-29 I. Bailleul , M. Hoshino

Probabilistic Circuits (PCs) are a promising avenue for probabilistic modeling. They combine advantages of probabilistic graphical models (PGMs) with those of neural networks (NNs). Crucially, however, they are tractable probabilistic…

Machine Learning · Computer Science 2021-06-07 Anji Liu , Guy Van den Broeck
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