Stochastic Partial Differential Equations

Taking readers with a basic knowledge of probability and real analysis to the frontiers of a very active research discipline, this textbook provides all the necessary background from functional analysis and the theory of PDEs. It covers the main types of equations (elliptic, hyperbolic and parabolic) and discusses different types of random forcing. The objective is to give the reader the necessary tools to understand the proofs of existing theorems about SPDEs (from other sources) and perhaps even to formulate and prove a few new ones. Most of the material could be covered in about 40 hours of lectures, as long as not too much time is spent on the general discussion of stochastic analysis in infinite dimensions.

As the subject of SPDEs is currently making the transition from the research level to that of a graduate or even undergraduate course, the book attempts to present enough exercise material to fill potential exams and homework assignments. Exercises appear throughout and are usually directly connected to the material discussed at a particular place in the text. The questions usually ask to verify something, so that the reader already knows the answer and, if pressed for time, can move on. Accordingly, no solutions are provided, but there are often hints on how to proceed.

The book will be of interest to everybody working in the area of stochastic analysis, from beginning graduate students to experts in the field.

Covers material for about 40 hours of lectures for everybody working in the area of stochastic analysis, from beginning graduate students to experts in the field Presents exercise material to fill potential exams and homework assignments, and gives hints that can lead to solutions. Provides all the necessary background from functional analysis and the theory of PDEs Gives all necessary tools to understand the proofs of existing theorems about SPDEs (from other sources) and even to formulate and prove a few new ones

Autorentext

Boris Rozovsky earned a Master's degree in Probability and Statistics, followed by a PhD in Physical and Mathematical Sciences, both from the Moscow State (Lomonosov) University. He was Professor of Mathematics and Director of the Center for Applied Mathematical Sciences at the University of Southern California. Currently, he is the Ford Foundation Professor of Applied Mathematics at Brown University. Sergey Lototsky earned a Master's degree in Physics in 1992 from the Moscow Institute of Physics and Technology, followed by a PhD in Applied Mathematics in 1996 from the University of Southern California. After a year-long post-doc at the Institute for Mathematics and its Applications and a three-year term as a Moore Instructor at MIT, he returned to the department of Mathematics at USC as a faculty member in 2000. He specializes in stochastic analysis, with emphasis on stochastic differential equation. He supervised more than 10 PhD students and had visiting positions at the Mittag-Leffler Institute in Sweden and at several universities in Israel and Italy.

Inhalt
Introduction.- Basic Ideas.- Stochastic Analysis in Infinite Dimensions.- Linear Equations: Square-Integrable Solutions.- The Polynomial Chaos Method.- Parameter Estimation for Diagonal SPDEs.- Solutions.- References.- Index.