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Aimed at graduate students, this book benefits from the author's research expertise and extensive teaching experience,... Read More >>
The author applies generalized fractional differentiation techniques of Riemann–Liouville, Caputo and Canavati types... Read More >>
This book presents a comprehensive account of the renormalization-group (RG) method and its extension, the doublet... Read More >>
How can a graphic novel teach you to solve physics problems? By making the process simply more fun, and more engaging... Read More >>
This book addresses the COVID-19 pandemic from a quantitative perspective based on mathematical models and methods... Read More >>
This highly interdisciplinary book, covering more than six fields, from philosophy and sciences all the way up to... Read More >>
The book focuses on the nonlinear dynamics based on the vector fields with bivariate quadratic functions. Such a... Read More >>
The presence of intense light waves in optoelectronics and strong electric field in nano-devices change the band... Read More >>
This concise and up-to-date textbook provides an accessible introduction to the core concepts of nonlinear dynamics... Read More >>
This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of... Read More >>
This book uses new ideas and language for understanding how self-organization and complexity trend toward increased... Read More >>
And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine... Read More >>
This book provides a complete introduction into spatial networks. Read More >>
This book presents a comprehensive review of various aspects of the novel and rapidly developing field of active... Read More >>
In chapter seven, there are several important application of quantum mechanics: the quantum particle in a box, the... Read More >>
The theory of data assimilation and machine learning is introduced in an accessible and pedagogical manner, with... Read More >>
The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic... Read More >>
Many people, including physicists, are confused about what the Second Law of thermodynamics really means, about... Read More >>
