Geometry and Invariance in Stochastic Dynamics

Geometry and Invariance in Stochastic Dynamics
Author :
Publisher : Springer Nature
Total Pages : 273
Release :
ISBN-10 : 9783030874322
ISBN-13 : 303087432X
Rating : 4/5 (22 Downloads)

Book Synopsis Geometry and Invariance in Stochastic Dynamics by : Stefania Ugolini

Download or read book Geometry and Invariance in Stochastic Dynamics written by Stefania Ugolini and published by Springer Nature. This book was released on 2022-02-09 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book grew out of the Random Transformations and Invariance in Stochastic Dynamics conference held in Verona from the 25th to the 28th of March 2019 in honour of Sergio Albeverio. It presents the new area of studies concerning invariance and symmetry properties of finite and infinite dimensional stochastic differential equations.This area constitutes a natural, much needed, extension of the theory of classical ordinary and partial differential equations, where the reduction theory based on symmetry and invariance of such classical equations has historically proved to be very important both for theoretical and numerical studies and has given rise to important applications. The purpose of the present book is to present the state of the art of the studies on stochastic systems from this point of view, present some of the underlying fundamental ideas and methods involved, and to outline the main lines for future developments. The main focus is on bridging the gap between deterministic and stochastic approaches, with the goal of contributing to the elaboration of a unified theory that will have a great impact both from the theoretical point of view and the point of view of applications. The reader is a mathematician or a theoretical physicist. The main discipline is stochastic analysis with profound ideas coming from Mathematical Physics and Lie’s Group Geometry. While the audience consists essentially of academicians, the reader can also be a practitioner with Ph.D., who is interested in efficient stochastic modelling.


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