Stochastic Averaging

The stochastic averaging methods are among the most effective and widely applied approximate methods for studying nonlinear stochastic dynamics. Upon an overview of global research on the subject, the book highlights a comprehensive summary of research results obtained by the group led by Professor Weiqiu Zhu at Zhejiang University in China and the group led by Professors Y. K. Lin and G. Q. Cai at Florida Atlantic University in the USA over the past three decades. The books are structured to progress logically from foundational principles to simple problems and then to increasingly complex applications. To facilitate understanding and mastery of the methods, the books offer essential preliminary knowledge and a wealth of examples.

The book comprises two volumes. Volume 1 introduces the basic principles of stochastic averaging methods and their applications to single-degree-of-freedom systems under various random excitations. It also covers stochastic averaging methods for quasi-Hamiltonian systems subjected to different random excitations, including Gaussian white noise, combined Gaussian and Poisson white noises, and fractional Gaussian noise. Volume 2 explores stochastic averaging methods for quasi-integrable Hamiltonian systems under colored noise excitation, quasi-integrable Hamiltonian systems with genetic effects under Gaussian white noise and colored noise excitations, and quasi-generalized Hamiltonian systems under Gaussian white noise excitation. Additionally, it covers applications of these methods in ecosystems and some other natural science and engineering scenarios.

These books serve as both introductory texts and valuable reference resources for readers in higher education and research institutions who are interested in or actively engaged in research involving nonlinear stochastic dynamics. The fields covered include mechanics, physics, chemistry, biology, ecology, astronautics and aeronautics, oceanography, civil engineering, mechanical engineering, and electrical engineering.