Many areas of applied mathematics call for an efficient calculus in infinite dimensions. This is most apparent in quantum physics and in all disciplines of science which describe natural phenomena by equations involving stochasticity. With this monograph we intend to provide a framework for analysis in infinite dimensions which is flexible enough to be applicable in many areas, and which on the other hand is intuitive and efficient. Whether or not we achieved our aim must be left to the judgment of the reader. This book treats the theory and applications of analysis and functional analysis in infinite dimensions based on white noise. By white noise we mean the generalized Gaussian process which is (informally) given by the time derivative of the Wiener process, i.e., by the velocity of Brownian mdtion. Therefore, in essence we present analysis on a Gaussian space, and applications to various areas of sClence. Calculus, analysis, and functional analysis in infinite dimensions (or dimension-free formulations of these parts of classical mathematics) have a long history. Early examples can be found in the works of Dirichlet, Euler, Hamilton, Lagrange, and Riemann on variational problems. At the beginning of this century, Frechet, Gateaux and Volterra made essential contributions to the calculus of functions over infinite dimensional spaces. The important and inspiring work of Wiener and Levy followed during the first half of this century. Moreover, the articles and books of Wiener and Levy had a view towards probability theory.

The intensive exchange between mathematicians and users has led in recent years to a rapid development of stochastic analysis. Of the users, the physicists form perhaps the most important group, giving direction to the mathematicians' research and providing a source of intuition. White noise analysis has emerged as a viable framework for stochastic and infinite dimensional analysis. Another growth area is the theory of stochastic partial differential equations. Gauge field theories are attracting increasing attention. Dirichlet forms provide a fruitful link between the mathematics of Markov processes and the physics of quantum systems. The deterministic-stochastic interface is addressed, as are Euclidean quantum mechanics, excursions of diffusions and the convergence of Markov chains to thermal states.

Stochastic analysis and its various applications in physics have to a large extent developed symbiotically. In the past decades mathematics has provided physics witb a vast and rapidly expanding array of tools and methods, while on the other hand physics has counted among the sources of direction and of structural intuition for the mathematical research in stochastics. We hope to have captured some of the focal points of this dialogue in the NATO AS! "Stochastic Analysis and its Applications in Physics" and in the present volume. On the mathematical side White Noise Analysis has emerged as a viable frame* work for stochastic and infinite dimensional analysis (Hida, Streit). Another growth point is the theory of stochastic partial differential equations and their applications (8ertini et aI., 0ksendal, Potthoff, Russo, Sinior). Gauge field theories have in- creasingly attracted the attention not only of physicists but of mathematicians as well (Gross, Liandre, Sengupta). On the other hand the contributions of Lang and of Vilela Mendes show the extent to which stochastic methods have found a place in the physicists' toolbox. Dirichlet forms provide a fruitful link between the mathematics of Markov processes and fields and the physics of quantum systems (Albeverio et al.). The deterministic-stochastic nterface i was addressed by Collet and by Mandrekar, Euclidean quantum mechanics by Cruzeiro and Zambrini, excursions of diffusions by Truman, and Kubo-Martin*Schwinger norms of statistical mechanics by Streater. So much for a rapid synopsis of the material represented in the present volume.

Many areas of applied mathematics call for an efficient calculus in infinite dimensions. This is most apparent in quantum physics and in all disciplines of science which describe natural phenomena by equations involving stochasticity. With this monograph we intend to provide a framework for analysis in infinite dimensions which is flexible enough to be applicable in many areas, and which on the other hand is intuitive and efficient. Whether or not we achieved our aim must be left to the judgment of the reader. This book treats the theory and applications of analysis and functional analysis in infinite dimensions based on white noise. By white noise we mean the generalized Gaussian process which is (informally) given by the time derivative of the Wiener process, i.e., by the velocity of Brownian mdtion. Therefore, in essence we present analysis on a Gaussian space, and applications to various areas of sClence. Calculus, analysis, and functional analysis in infinite dimensions (or dimension-free formulations of these parts of classical mathematics) have a long history. Early examples can be found in the works of Dirichlet, Euler, Hamilton, Lagrange, and Riemann on variational problems. At the beginning of this century, Frechet, Gateaux and Volterra made essential contributions to the calculus of functions over infinite dimensional spaces. The important and inspiring work of Wiener and Levy followed during the first half of this century. Moreover, the articles and books of Wiener and Levy had a view towards probability theory.