Rough Paths above Weierstrass Functions

Rough paths theory allows for a pathwise theory of solutions to differential equations driven by highly irregular signals. The fundamental observation of rough paths theory is that if one can define “iterated integrals” above a signal, then one can construct solutions to differential equations drive...

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Main Authors: Cellarosi, Francesco, Selk, Zachary
Format: Article
Language:English
Published: Académie des sciences 2024-11-01
Series:Comptes Rendus. Mathématique
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Online Access:https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.635/
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author Cellarosi, Francesco
Selk, Zachary
author_facet Cellarosi, Francesco
Selk, Zachary
author_sort Cellarosi, Francesco
collection DOAJ
description Rough paths theory allows for a pathwise theory of solutions to differential equations driven by highly irregular signals. The fundamental observation of rough paths theory is that if one can define “iterated integrals” above a signal, then one can construct solutions to differential equations driven by the signal.The typical examples of the signals of interest are stochastic processes such as (fractional) Brownian motion. However, rough paths theory is not inherently random and therefore can treat irregular deterministic driving signals such as a (vector-valued) Weierstrass function. This note supplies a construction of a rough path above vector-valued Weierstrass functions.
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institution Kabale University
issn 1778-3569
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publishDate 2024-11-01
publisher Académie des sciences
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series Comptes Rendus. Mathématique
spelling doaj-art-3c4214e4b9024df48943401caf494ca52025-02-07T11:26:37ZengAcadémie des sciencesComptes Rendus. Mathématique1778-35692024-11-01362G121555157010.5802/crmath.63510.5802/crmath.635Rough Paths above Weierstrass FunctionsCellarosi, Francesco0Selk, Zachary1Department of Mathematics and Statistics, Queen’s University, CanadaDepartment of Mathematics and Statistics, Queen’s University, CanadaRough paths theory allows for a pathwise theory of solutions to differential equations driven by highly irregular signals. The fundamental observation of rough paths theory is that if one can define “iterated integrals” above a signal, then one can construct solutions to differential equations driven by the signal.The typical examples of the signals of interest are stochastic processes such as (fractional) Brownian motion. However, rough paths theory is not inherently random and therefore can treat irregular deterministic driving signals such as a (vector-valued) Weierstrass function. This note supplies a construction of a rough path above vector-valued Weierstrass functions.https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.635/Rough pathsWeierstrass functions
spellingShingle Cellarosi, Francesco
Selk, Zachary
Rough Paths above Weierstrass Functions
Comptes Rendus. Mathématique
Rough paths
Weierstrass functions
title Rough Paths above Weierstrass Functions
title_full Rough Paths above Weierstrass Functions
title_fullStr Rough Paths above Weierstrass Functions
title_full_unstemmed Rough Paths above Weierstrass Functions
title_short Rough Paths above Weierstrass Functions
title_sort rough paths above weierstrass functions
topic Rough paths
Weierstrass functions
url https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.635/
work_keys_str_mv AT cellarosifrancesco roughpathsaboveweierstrassfunctions
AT selkzachary roughpathsaboveweierstrassfunctions