Analysis For Diffusion Processes On Riemannian Manifolds

Stochastic analysis on Riemannian manifolds without boundary has been well established. However, the analysis for reflecting diffusion processes and sub-elliptic diffusion processes is far from complete. This book contains recent advances in this direction along with new ideas and efficient arguments, which are crucial for further developments. Many results contained here (for example, the formula of the curvature using derivatives of the semigroup) are new among existing monographs even in the case without boundary.

During the weekend of March 16-18, 1990 the University of North Carolina at Charlotte hosted a conference on the subject of stochastic flows, as part of a Special Activity Month in the Department of Mathematics. This conference was supported jointly by a National Science Foundation grant and by the University of North Carolina at Charlotte. Originally conceived as a regional conference for researchers in the Southeastern United States, the conference eventually drew participation from both coasts of the U. S. and from abroad. This broad-based par ticipation reflects a growing interest in the viewpoint of stochastic flows, particularly in probability theory and more generally in mathematics as a whole. While the theory of deterministic flows can be considered classical, the stochastic counterpart has only been developed in the past decade, through the efforts of Harris, Kunita, Elworthy, Baxendale and others. Much of this work was done in close connection with the theory of diffusion processes, where dynamical systems implicitly enter probability theory by means of stochastic differential equations. In this regard, the Charlotte conference served as a natural outgrowth of the Conference on Diffusion Processes, held at Northwestern University, Evanston Illinois in October 1989, the proceedings of which has now been published as Volume I of the current series. Due to this natural flow of ideas, and with the assistance and support of the Editorial Board, it was decided to organize the present two-volume effort.

I: Diffusion Processes and General Stochastic Flows on Manifolds.- Stability and equilibrium properties of stochastic flows of diffeomorphisms.- Stochastic flows on Riemannian manifolds.- II: Special Flows and Multipoint Motions.- Isotropic stochastic flows.- The existence of isometric stochastic flows for Riemannian Brownian motions.- Time-reversal of solutions of equations driven by Lévy processes.- Birth and death on a flow.- III: Infinite Dimensional Systems.- Lyapunov exponents and stochastic flows of linear and affine hereditary systems.- Convergence in distribution of Markov processes generated by i.i.d. random matrices.- IV: Invariant Measures in Real and White Noise-Driven Systems.- Remarks on ergodic theory of stochastic flows and control flows.- Stochastic bifurcation: instructive examples in dimension one.- Lyapunov exponent and rotation number of the linear harmonic oscillator.- The growth of energy of a free particle of small mass with multiplicative real noise.- V: Iterated Function Systems.- Iterated function systems and multiplicative ergodic theory.- Weak convergence and generalized stability for solutions to random dynamical systems.- Random affine iterated function systems: mixing and encoding.

The book covers the latest research in the areas of mathematics that deal the properties of partial differential equations and stochastic processes on spaces in connection with the geometry of the underlying space. Written by experts in the field, this book is a valuable tool for the advanced mathematician.

Stochastic differential equations, and Hoermander form representations of diffusion operators, can determine a linear connection associated to the underlying (sub)-Riemannian structure. This is systematically described, together with its invariants, and then exploited to discuss qualitative properties of stochastic flows, and analysis on path spaces of compact manifolds with diffusion measures. This should be useful to stochastic analysts, especially those with interests in stochastic flows, infinite dimensional analysis, or geometric analysis, and also to researchers in sub-Riemannian geometry. A basic background in differential geometry is assumed, but the construction of the connections is very direct and itself gives an intuitive and concrete introduction. Knowledge of stochastic analysis is also assumed for later chapters.

Concerned with probability theory, Elton Hsu's study focuses primarily on the relations between Brownian motion on a manifold and analytical aspects of differential geometry. A key theme is the probabilistic interpretation of the curvature of a manifold

Annotation Contents: G. Benarous: Noyau de la chaleur hypoelliptique et géométrie sous-riemannienne.- M. Fukushima: On two Classes of Smooth Measures for Symmetric Markov Processes.- T. Funaki: The Hydrodynamical Limit for Scalar Ginzburg-Landau Model on R.- N. Ikeda, S. Kusuoka: Short time Asymptotics for Fundamental Solutions of Diffusion Equations.- K. Ito: Malliavin Calculus on a Segal Space.- Y. Kasahara, M. Maejima: Weak Convergence of Functionals of Point Processes on Rd.- Y. Katznelson, P. Malliavin: Image des Points critiques d'une application régulière.- S. Kusuoka: Degree Theorem in Certain Wiener Riemannian Manifolds.- R. Leandre: Applications quantitatives et géométrique du calcul de Malliavin.- Y. Le Jan: On the Fock Space Representation of Occupations Times for non Reversible Markov Processes.- M. Metivier, M. Viot: On Weak Solutions of Stochastic Partial Differential Equations.- P.A. Meyer: Une remarque sur les Chaos de Wiener.- H. Tanaka: Limit Theorem for One-Dimensional Diffusion Process in Brownian Environment.- H. Uemura, S. Watanabe: Diffusion Processes and Heat Kernels on Certain Nilpotent Groups.

In July 1987, an AMS-IMS-SIAM Joint Summer Research Conference on Geometry of Random Motion was held at Cornell University. The initial impetus for the meeting came from the desire to further explore the now-classical connection between diffusion processes and second-order (hypo)elliptic differential operators. To accomplish this goal, the conference brought together leading researchers with varied backgrounds and interests: probabilists who have proved results in geometry, geometers who have used probabilistic methods, and probabilists who have studied diffusion processes. Focusing on the interplay between probability and differential geometry, this volume examines diffusion processes on various geometric structures, such as Riemannian manifolds, Lie groups, and symmetric spaces. Some of the articles specifically address analysis on manifolds, while others center on (nongeometric) stochastic analysis. The majority of the articles deal simultaneously with probabilistic and geometric techniques. Requiring a knowledge of the modern theory of diffusion processes, this book will appeal to mathematicians, mathematical physicists, and other researchers interested in Brownian motion, diffusion processes, Laplace-Beltrami operators, and the geometric applications of these concepts. The book provides a detailed view of the leading edge of research in this rapidly moving field.