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We develop a solution theory for singular elliptic stochastic PDEs with fractional Laplacian, additive white noise and cubic non-linearity. The method covers the whole sub-critical regime. It is based on the Wilsonian renormalization group…

Probability · Mathematics 2025-02-12 Paweł Duch

We prove universality of a macroscopic behavior of solutions of a large class of semi-linear parabolic SPDEs on $\mathbb{R}_+\times\mathbb{T}$ with fractional Laplacian $(-\Delta)^{\sigma/2}$, additive noise and polynomial non-linearity,…

Probability · Mathematics 2025-03-19 Paweł Duch

We discuss the exact renormalization group or flow equation for the effective action and its decomposition into one particle irreducible N point functions. With the help of a truncated flow equation for the four point function we study the…

High Energy Physics - Phenomenology · Physics 2008-11-26 Ulrich Ellwanger

In this work, we study the renormalisation of singular SPDEs in the flow approach recently developed by Duch. We introduce a general ansatz based on decorated trees for the solution of the flow equation. The ansatz is renormalised in a…

Probability · Mathematics 2026-05-26 Yvain Bruned , Aurélien Minguella

The flow equation method was proposed by Wegner as a technique for studying interacting systems in one dimension. Here, we apply this method to a disordered one dimensional model with power-law decaying hoppings. This model presents a…

Disordered Systems and Neural Networks · Physics 2016-09-21 Victor L. Quito , Paraj Titum , David Pekker , Gil Refael

Many systems in physics, engineering, and biology exhibit multiscale stochastic dynamics, where low-dimensional slow variables evolve under the influence of high-dimensional fast processes. In practice, observations are often limited to a…

Machine Learning · Statistics 2026-05-12 Anan Saha , Arnab Ganguly

We investigate the use of renormalisation group methods to solve partial differential equations (PDEs) numerically. Our approach focuses on coarse-graining the underlying continuum process as opposed to the conventional numerical analysis…

Statistical Mechanics · Physics 2009-10-31 Nigel Goldenfeld , Alan McKane , Qing Hou

The flow equation approach investigated by Wegner et al. is applied to an unbounded Hamiltonian system with a generalization. We show that a well-known quantized complex energy eigenvalues which is related to decay widths can be given with…

Quantum Physics · Physics 2009-11-07 Yukiko Ohira , Kentaro Imafuku

We develop a formalism to describe the formation of bound states in quantum field theory using an exact renormalization group flow equation. As a concrete example we investigate a nonrelativistic field theory with instantaneous interaction…

High Energy Physics - Theory · Physics 2010-10-04 S. Floerchinger

The theory of Wasserstein gradient flows in the space of probability measures has made an enormous progress over the last twenty years. It constitutes a unified and powerful framework in the study of dissipative partial differential…

Analysis of PDEs · Mathematics 2022-01-17 Daniel Adams , Manh Hong Duong , Goncalo dos Reis

We show within the Wilson renormalization group framework how the flow equation method can be used to prove the perturbative renormalizability of a relativistic massive selfinteracting scalar field. Furthermore we prove the regularity of…

High Energy Physics - Theory · Physics 2007-05-23 Georg Keller , Christoph Kopper , Clemens Schophaus

The goal of this thesis is the development and implementation of a non-perturbative solution method for Wegner's flow equations. We show that a parameterization of the flowing Hamiltonian in terms of a scalar function allows the flow…

Other Condensed Matter · Physics 2009-11-11 J. N. Kriel

We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differential equations (SDEs). Using the theory of rough paths, the…

Machine Learning · Statistics 2020-02-27 Liam Hodgkinson , Chris van der Heide , Fred Roosta , Michael W. Mahoney

A new form of the Wilson renormalization group equation is derived, in which the flow equations are, up to linear terms, proportional to a gradient flow. A set of co\"ordinates is found in which the flow of marginal, low-energy, couplings…

High Energy Physics - Theory · Physics 2008-02-03 Robert C. Myers , Vipul Periwal

We propose a renormalization group flow equation for a functional that generates $S$-matrix elements and which captures similarities to the well-known Wetterich and Polchinski equations. While the latter ones respectively involve the…

High Energy Physics - Theory · Physics 2025-09-09 Laurent Freidel , José Padua-Argüelles , Susanne Schander , Marc Schiffer

We present a discretization-free scalable framework for solving a large class of mass-conserving partial differential equations (PDEs), including the time-dependent Fokker-Planck equation and the Wasserstein gradient flow. The main…

Machine Learning · Computer Science 2023-11-15 Lingxiao Li , Samuel Hurault , Justin Solomon

This paper illustrates the Renormalisation Group (RG) approach to singular SPDEs following a framework introduced by Kupiainen \cite{Kupiainen2016}. We study a linear elliptic SPDE with a hierarchical Laplace operator and multiplicative…

Probability · Mathematics 2026-03-19 Leonard Ferdinand , Simon Gabriel

We present an equation-free dynamic renormalization approach to the computational study of coarse-grained, self-similar dynamic behavior in multidimensional particle systems. The approach is aimed at problems for which evolution equations…

Dynamical Systems · Mathematics 2009-11-11 Yu Zou , Ioannis Kevrekidis , Roger Ghanem

We adapt the precise definition of the flowing effective action in order to obtain a functional flow equation with simple properties close to physical intuition. The simplified flow equation is invariant under local gauge transformations…

High Energy Physics - Theory · Physics 2025-04-09 C. Wetterich

Stochastic processes generated by non-stationary distributions are difficult to represent with conventional models such as Gaussian processes. This work presents Recurrent Autoregressive Flows as a method toward general stochastic process…

Machine Learning · Computer Science 2020-06-20 John Mern , Peter Morales , Mykel J. Kochenderfer
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