arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2307.03036math.PRmath.APmath.RA
收藏

基于多重指标的正则性结构中代数重整化的自上而下方法

A top-down approach to algebraic renormalization in regularity structures based on multi-indices

Yvain Bruned, Pablo Linares

AI总结:

本文提出一种自上而下的代数重整化框架,通过推广导数Hopf代数并利用指数映射构建典范光滑模型,适用于一大类半线性随机偏微分方程。

摘要:

我们提供了一个代数框架,用于描述基于多重指标的正则性结构中的重整化,适用于一大类半线性随机偏微分方程。该框架是“自上而下”的,即我们假定反项的形式,并利用重整化方程为其构建一个典范光滑模型。构造的核心是对[LOT23]中导数Hopf代数的推广,将其扩展到结构群之外,通过指数映射来描述模型方程:这使得我们能够实现一种重整化程序,该程序类似于我们语境中的预备映射方法。

英文摘要:

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 counterterm and use the renormalized equation to build a canonical smooth model for it. The core of the construction is a generalization of the Hopf algebra of derivations in [LOT23], which is extended beyond the structure group to describe the model equation via an exponential map: This allows to implement a renormalization procedure which resembles the preparation map approach in our context.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑