Information Physics: Physics-Information and Quantum Analogies for Complex Modeling presents a new theory of complex systems that uses analogy across various aspects of physics, including electronics, magnetic circuits and quantum mechanics. The book explains the quantum approach to system theory that can be understood as an extension of classical system models. The main idea is that in many complex systems there are incomplete pieces of overlapping information that must be strung together to find the most consistent model. This incomplete information can be understood as a set of non-exclusive observer results. Because they are non-exclusive, each observer registers different pictures of reality.
Dr. Miroslav Svitek is a full professor in Engineering Informatics at Faculty of Transportation Sciences, Czech Technical University in Prague. He has been the Dean of Faculty of Transportation Sciences, Czech Technical University. Since 2018, he has been a Visiting Professor in Smart Cities at University of Texas at El Paso, USA. The focus of his research includes complex system sciences and their practical applications to Intelligent Transport Systems, Smart Cities and Smart Regions. He is the author or co-author of more than 200 scientific papers and 10 books, including Quantum System Theory: Principles and Applications and Stochastic Processes: Estimation, Optimisation, and Analysis. He received his Ph.D. in radioelectronics at Faculty of Electrical Engineering, Czech Technical University. In 2005, he was been nominated as the extraordinary professor in applied informatics at Faculty of Natural Sciences, University of Matej Bel in Banska Bystrica, Slovak Republic. In 2008, Dr. Svitek was the first president of the Czech Smart City Cluster, and he is a member of the Engineering Academy of the Czech Republic. In 2006 - 2018, he served as President of the Association of Transport Telematics
Title: Information Physics: Physics-Information and Quantum Analogies for Complex Systems Modeling
Author: Svitek, Miroslav
ISBN: 9780323910118
Binding:
Publisher: Elsevier Science & Technology
Publication Date: 2021-06-14
Number of Pages: 154
Weight: 0.3401 kg
