Ramakant Bhardwaj – författare

MATHEMATICS IN COMPUTATIONAL SCIENCE AND ENGINEERING This groundbreaking new volume, written by industry experts, is a must-have for engineers, scientists, and students across all engineering disciplines working in mathematics and computational science who want to stay abreast with the most current and provocative new trends in the industry. Applied science and engineering is the application of fundamental concepts and knowledge to design, build and maintain a product or a process, which provides a solution to a problem and fulfills a need. This book contains advanced topics in computational techniques across all the major engineering disciplines for undergraduate, postgraduate, doctoral and postdoctoral students. This will also be found useful for professionals in an industrial setting. It covers the most recent trends and issues in computational techniques and methodologies for applied sciences and engineering, production planning, and manufacturing systems. More importantly, it explores the application of computational techniques and simulations through mathematics in the field of engineering and the sciences. Whether for the veteran engineer, scientist, student, or other industry professional, this volume is a must-have for any library. Useful across all engineering disciplines, it is a multifactional tool that can be put to use immediately in practical applications. This groundbreaking new volume: Includes detailed theory with illustrations Uses an algorithmic approach for a unique learning experiencePresents a brief summary consisting of concepts and formulaeIs pedagogically designed to make learning highly effective and productive Is comprised of peer-reviewed articles written by leading scholars, researchers and professorsAUDIENCE: Engineers, scientists, students, researchers, and other professionals working in the field of computational science and mathematics across multiple disciplines

Mathematics for Engineers serves as a comprehensive guide on the basics of mathematics and their applications in engineering for students and seasoned professionals alike. Mathematics for Engineers is designed to help students develop mathematical proficiencies, which are required in technical courses and careers involving strategic mathematical competence and adaptive reasoning. This volume also acts as a reference for professionals in engineering who need a refresher for their technical math skills. Through this book, students and professionals in the engineering discipline will build a capacity and expand their fundamental mathematical skills for logical thought, reflection, explanation, and justification in the field of applied science. This book is designed for general use for science and engineering students across the globe. The book effectively compiles important information in one place alongside examples and practice problems with application and practice based questions. Emphasis is placed on the application of mathematics in appropriate context and modeling of real-world situations. By the end of this book, students and professionals in the engineering discipline will be able to present and process their mathematical reasoning and conclusions numerically, graphically, symbolically, and verbally.

The book provides comprehensive and cognitive approach to building and deploying sophisticated information systems. The book utilizes non-linear optimization techniques, fuzzy logic, and rough sets to model various real-world use cases for the digital era. The hybrid information system modeling handles both qualitative and quantitative data and can effectively handle uncertainty and imprecision in the data. The combination of non-linear optimization mechanisms, fuzzy logic, and rough sets provides a robust foundation for next-generation information systems that can fulfill the demands of adaptive, aware, and adroit software applications for the knowledge era. The book emphasizes the importance of the hybrid approach, which combines the strengths of both mathematical and AI techniques, to achieve a more comprehensive and effective modeling process. Hybrid information system modeling techniques combine different approaches, such as fuzzy logic, rough sets, and neural networks, to create models that can handle the complexity and uncertainty of real-world problems. These techniques provide a powerful tool for modeling and analyzing complex systems, and the applications of hybrid information system modeling demonstrate their potential for solving real-world problems in various fields.