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Related papers: Flows driven by multi-indices Rough Paths

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In this note we consider differential equations driven by a signal $x$ which is $\gamma$-H\"older with $\gamma>1/3$, and is assumed to possess a lift as a rough path. Our main point is to obtain existence of solutions when the coefficients…

Probability · Mathematics 2017-08-17 Prakash Chakraborty , Samy Tindel

In recent years, substantial progress was made towards understanding convergence of fast-slow deterministic systems to stochastic differential equations. In contrast to more classical approaches, the assumptions on the fast flow are very…

Dynamical Systems · Mathematics 2019-06-18 Ilya Chevyrev , Peter K. Friz , Alexey Korepanov , Ian Melbourne , Huilin Zhang

The existence of unique solutions is established for rough differential equations (RDEs) with path-dependent coefficients and driven by c\`adl\`ag rough paths. Moreover, it is shown that the associated solution map, also known as…

Probability · Mathematics 2025-08-26 Anna P. Kwossek , Andreas Neuenkirch , David J. Prömel

A branched rough path $X$ consists of a rough integral calculus for $X \colon [0, T] \to \mathbb R^d$ which may fail to satisfy integration by parts. Using Kelly's bracket extension [Kel12], we define a notion of pushforward of branched…

Classical Analysis and ODEs · Mathematics 2023-11-29 Emilio Ferrucci

We consider rough differential equations whose coefficients contain path-dependent bounded variation terms and prove the existence and a priori estimate of solutions. These equations include classical path-dependent SDEs containing running…

Probability · Mathematics 2024-03-12 Shigeki Aida

We consider the rough differential equation with drift driven by a Gaussian geometric rough path. Under natural conditions on the rough path, namely non-determinism, and uniform ellipticity conditions on the diffusion coefficient, we prove…

Probability · Mathematics 2024-02-15 Rémi Catellier , Romain Duboscq

We explore the limit of stochastic differential equations driven by some random processes satisfying singularly perturbed second order stochastic differential equations. The main tool we employ is the universal limit theorem in rough path…

Probability · Mathematics 2026-04-08 Qingming Zhao , Xueru Liu , Wei Wang

We apply the parabolic flow method to solving complex quotient equations on closed K\"ahler manifolds. We study the parabolic equation and prove the convergence. As a result, we solve the complex quotient equations.

Analysis of PDEs · Mathematics 2017-12-05 Wei Sun

We propose a theory of linear differential equations driven by unbounded operator-valued rough signals. As an application we consider rough linear transport equations and more general linear hyperbolic symmetric systems of equations driven…

Analysis of PDEs · Mathematics 2017-06-27 I. Bailleul , M. Gubinelli

We show that accelerated optimization methods can be seen as particular instances of multi-step integration schemes from numerical analysis, applied to the gradient flow equation. In comparison with recent advances in this vein, the…

Optimization and Control · Mathematics 2017-02-23 Damien Scieur , Vincent Roulet , Francis Bach , Alexandre d'Aspremont

We consider the solution of initial value problems within the context of hybrid systems and emphasise the use of high precision approximations (in software for exact real arithmetic). We propose a novel algorithm for the computation of…

Mathematical Software · Computer Science 2010-06-03 Norbert Th. Müller , Margarita Korovina

We establish the existence of solutions to path-dependent rough differential equations with non-anticipative coefficients. Regularity assumptions on the coefficients are formulated in terms of horizontal and vertical derivatives.

Probability · Mathematics 2020-01-30 Anna Ananova

We consider flows of ordinary differential equations (ODEs) driven by path differentiable vector fields. Path differentiable functions constitute a proper subclass of Lipschitz functions which admit conservative gradients, a notion of…

Machine Learning · Computer Science 2022-01-12 Swann Marx , Edouard Pauwels

The goal of these notes is to provide an introduction to rough partial differential equations. For this purpose, we will present the theory of rough paths to the extend as it is required. Applications to stochastic partial differential…

Probability · Mathematics 2026-05-12 Stefan Tappe

In this article we extend the framework of rough paths to processes of variable H\"older exponent or variable order paths. We show how a class of multiple discrete delay differential equations driven by signals of variable order are…

Probability · Mathematics 2018-10-04 Fabian A. Harang

We introduce a gradient flow formulation of linear Boltzmann equations. Under a diffusive scaling we derive a diffusion equation by using the machinery of gradient flows.

Mathematical Physics · Physics 2020-09-03 Giada Basile , Dario Benedetto , Lorenzo Bertini

T. Lyons' rough path theory is something like a deterministic version of K. Ito's theory of stochastic differential equations, combined with ideas from K. T. Chen's theory of iterated path integrals. In this article we survey rough path…

Probability · Mathematics 2016-02-11 Yuzuru Inahama

Existence and uniqueness for rough flows, transport and continuity equations driven by general geometric rough paths are established.

Analysis of PDEs · Mathematics 2021-10-05 Carlo Bellingeri , Ana Djurdjevac , Peter K. Friz , Nikolas Tapia

In this article we address flow problems that carry a multiscale character in time. In particular we consider the Navier-Stokes flow in a channel on a fast scale that influences the movement of the boundary which undergoes a deformation on…

Numerical Analysis · Mathematics 2020-08-11 Stefan Frei , Thomas Richter

Real world networks are often subject to severe uncertainties which need to be addressed by any reliable prescriptive model. In the context of the maximum flow problem subject to arc failure, robust models have gained particular attention.…

Discrete Mathematics · Computer Science 2017-05-24 Fabian Mies , Britta Peis , Andreas Wierz