A Guide to Generalized Functions: Linear, Nonlinear, Random, and Infinite Dimensional Distributions

A Guide to Generalized Functions: Linear, Nonlinear, Random, and Infinite Dimensional Distributions book cover

A Guide to Generalized Functions: Linear, Nonlinear, Random, and Infinite Dimensional Distributions

Author(s): Uluğ Çapar (Author)

  • Publisher: Birkhäuser
  • Publication Date: June 2, 2026
  • Language: English
  • Print length: 270 pages
  • ISBN-10: 3032091837
  • ISBN-13: 9783032091833

Book Description

The aim of the present work is to give a unifying treatment of the four faces of the theory of generalized functions – linear, nonlinear, random and infinite dimensional distributions. The book deals with all of these categories in a comparative and interconnected way, giving a broad overview without getting lost in too many details. This original presentation touches upon a number of mathematical areas, such as functional analysis, theory of functions, measure theory, operator theory, differentiable manifolds, probability theory, stochastic processes and stochastic analysis. For example, in one of the chapters it builds a bridge from Gaussian measures on Hilbert spaces, Malliavin calculus, Wiener chaos, Meyer-Watanabe distributions, Hida distributions and White Noise Analysis up to Kondratiev spaces, ending with Colombeau versions. Any researcher who is looking for an overview on the state-of-the art, or to specialize in the theory of distributions, will find this book a useful resource.

Editorial Reviews

From the Back Cover

The aim of the present work is to give a unifying treatment of the four faces of the theory of generalized functions – linear, nonlinear, random and infinite dimensional distributions. The book deals with all of these categories in a comparative and interconnected way, giving a broad overview without getting lost in too many details. This original presentation touches upon a number of mathematical areas, such as functional analysis, theory of functions, measure theory, operator theory, differentiable manifolds, probability theory, stochastic processes and stochastic analysis. For example, in one of the chapters it builds a bridge from Gaussian measures on Hilbert spaces, Malliavin calculus, Wiener chaos, Meyer-Watanabe distributions, Hida distributions and White Noise Analysis up to Kondratiev spaces, ending with Colombeau versions. Any researcher who is looking for an overview on the state-of-the art, or to specialize in the theory of distributions, will find this book a useful resource.

About the Author

Uluğ Çapar is an emeritus professor of mathematics from Sabancı University in Istanbul, Turkey. His first degree was in engineering with continuum mechanics as option. Two professors influenced him to reshape his professional career as a mathematician. The first one was the most celebrated Turkish mathematician Cahit Arf. The second one was Jan Mikusinski, a leading figure of the theory of generalized functions (distributions). The subject has become part of the author’s lifetime academic occupation except that his interests have shifted in the long run to its nonlinear and random versions. He was a student in graduate courses offered by both of them. Prof. Arf would also become his colleague in his last years at the Middle East Technical University (M.E.T.U.), Ankara.

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