When confronted with a nonlinear systems engineering problem, the first approach usually
is to linearize; in other words, to try to avoid the nonlinear aspects of the problem. It is indeed …

The theory of random measures is a key area of modern probability theory, arguably as rich
and important as martingale theory, ergodic theory, or probabilistic potential theory, to …

The concept of Wiener chaos generalizes to an infinite-dimensional setting the properties of
orthogonal polynomials associated with probability distributions on the real line. It plays a …

The Stochastic Finite Element Method has seen a number of incarnations over the past few
years, ranging from perturbation-based methods, and Neumann-expansion based methods …

This state-of-the-art report assesses the current state of development of computational
procedures as utilized in stochastic mechanics. The theoretical developments and aspects …

This paper is a tutorial of nonlinear system modeling methods which are based on the
Wiener theory of nonlinear systems. The basic concepts that underlie the Wiener theory are …

This monograph is based on lectures given by the author at the National Science
Foundation—Conference Board of the Mathematical Sciences Regional Conference in June …

The present bibliography represents a comprehensive list of references on nonlinear system
identification and its applications in signal processing, communications, and biomedical …

The fundamental importance of functional differential equations has been recognized in
many areas of mathematical physics, such as fluid dynamics (Hopf characteristic functional …

Volterra and Wiener series are perhaps the best-understood nonlinear system
representations in signal processing. Although both approaches have enjoyed a certain …