Mathematical Modeling and Applications in Nonlinear Dynamics, Softcover reprint of the original 1st ed. 2016 Nonlinear Systems and Complexity Series, Vol. 14

The book covers nonlinear physical problems and mathematical modeling, including molecular biology, genetics, neurosciences, artificial intelligence with classical problems in mechanics and astronomy and physics. The chapters present nonlinear mathematical modeling in life science and physics through nonlinear differential equations, nonlinear discrete equations and hybrid equations. Such modeling can be effectively applied to the wide spectrum of nonlinear physical problems, including the KAM (Kolmogorov-Arnold-Moser (KAM)) theory, singular differential equations, impulsive dichotomous linear systems, analytical bifurcation trees of periodic motions, and almost or pseudo- almost periodic solutions in nonlinear dynamical systems.

From the Contents: Introduction.- Mathematical Neuroscience: from neurons to networks.- Jupiters belts, our Ozone holes, and Degenerate tori.- Analytical solutions of periodic motions in time-delay systems.- DNA elasticity and its biological implications.- Epidemiology, dynamics, control and multi-patch mobility.

Albert C.J. Luo is a Professor in the Department of Mechanical and Industrial Engineering, South Illinois University Edwardsville, Edwardsville, IL USA. Hüseyin Merdan is a Professor in the the
Department of Mathematics, TOBB University of Economics and Technology, Ankara, TURKEY.

Provides methods for mathematical models with switching, thresholds, and impulses, each of particular importance for discontinuous processes Includes qualitative analysis of behaviors on Tumor-Immune Systems and methods of analysis for DNA, neural networks and epidemiology Introduces new concepts, methods, and applications in nonlinear dynamical systems covering physical problems and mathematical modeling relevant to molecular biology, genetics, neurosciences, artificial intelligence as well as classic problems in mechanics, astronomy, and physics Demonstrates mathematic modeling relevant to molecular biology, genetics, neurosciences, artificial intelligence as well as classic problems in mechanics, astronomy, and physics Includes supplementary material: sn.pub/extras