Snehashish Chakraverty Livres

Computational Wave Dynamics explains the analytical, semi-analytical, and numerical methods for finding exact or approximate solutions to various linear and nonlinear differential equations governing wave-like flows. The book explores the latest and most efficient linear and nonlinear differential equations that govern all waves with particular emphasis on water waves, helping the reader to incorporate a more profound numerical understanding of waves in a range of engineering solutions.  Procedures, algorithms, and solutions are presented in a simple step-by-step style, helping readers with different backgrounds at various levels to engage with this topic. Waves exist almost everywhere in nature. Different types include water, sound, electromagnetic, seismic, and shock. The breadth of different methods addressed in this one book creates a uniquely valuable resource for the comparison of equations, and acts as a very useful summary of recent research into computational wave dynamics.

Focusing on the vibration analysis of two-dimensional plates, this book serves as a comprehensive introduction to the theory and practical applications necessary for safe engineering design. Drawing from over 15 years of original research, it covers essential concepts and methods in plate vibration, making it an invaluable resource for engineers and researchers seeking to understand the dynamics of plate structures. The text is self-contained, ensuring accessibility for those new to the subject as well as seasoned professionals.

This book discusses soft computing, which provides an efficient platform to deal with imprecision, uncertainty, vagueness and approximation in order to attain robustness and reliable computing. It explores two major concepts of soft computing: fuzzy set theory and neural networks, which relate to uncertainty handling and machine learning techniques respectively. Generally, fuzzy sets are considered as vague or uncertain sets having membership function lying between 0 and 1, and ANN is a type of artificial intelligence that attempts to imitate the way a human brain works by configuring specific applications, for instance pattern recognition or data classification, through learning processes. The book also presents C/MATLAB programming codes related to the basics of fuzzy set, interval arithmetic and ANN in a concise, practical and adaptable manner along, with simple examples and self-validation unsolved practice questions in few cases