Engineering
"The difference between theory and practice is smaller in theory than it is in practice."
- Albert Einstein
- Albert Einstein
In engineering, as classical engineering is compared with complexity engineering, it is seen that the former deals with reducible systems within dividual systems and systems of systems. On the other hand, complexity engineering deals with systems with emergence within individual systems. Against this backdrop, application examples of complex systems engineering, for instance, software engineering with self-organizing aspects, large database systems, constructing artificial models to understand natural systems; electrical engineering with large scale traffic flow and management, robotics with open mobile robots and coalitions; production engineering with evolvable systems including supply networks and customers; biotechnology with man-made biological ecosystems, vaccines, tissue engineering, medical engineering, computer science, geological engineering, electrics and electronics engineering, mathematical engineering, among many others shed light on the profiles of some points. Due to the emerging challenges arising with big data, it has become ever important to connect to new systems to perform additional and augmented checks to protect data from malware and inconsistent manipulation.
The examination of complexity conveys the attempt to explore and find out the common principles that lie under the behavior of complex systems where systems with large collections of components interact in nonlinear ways. Nonlinearity suggests that it is not possible to understand it merely by understanding its individual components. Rather, collective sophistication needs to come about since nonlinear interactions make the whole be more than the sum of its parts. Those who learn about complexity are inspired by the similarities among the disparate systems with the systems manifesting self-organization, which means the components of the system organize themselves in a way to act as a coherent whole without the leverage of an outside or central controller. The encoding and processing information capability of complex systems shows their evolving and continuously changing nature in an open-ended manner, learning and adapting over time.
As for big data, complexity offers a sort of big theory which refers to the scientific understanding of the complex processes which generate the data to be needed. The seeking of complexity after big theory can help transform our understanding of the world and the universe as a breakthrough and in a profound way. Taken together, complex engineering and applications thereof will serve to achieve this by successive strategies and techniques based on mathematics-informed and / or physical- informed frameworks through the constitution of a global view while reducing cost, computational burden without disregarding the preservation of the ethical and value-creation aspects. Above all, the most recent application of new solution algorithms, should be covered along with numerical methods and solution techniques with innovative touch, geared towards the utilization of computational methods in design, practice and analysis in engineering. With quantitative classification means, it is aimed at investigating what resources are required (lower bounds) and what resources are adequate (upper bounds) for the solution of various problems. Additionally, it is important to note that transition from a society poor in terms of data to a data rich society characterizes a dramatic revolution how vast quantities of information is handled, processed and managed. This transition has also brought about complex problems which emerge in terms of interpreting enormous volume of data, which has also come with novel approaches and integrative ideas inspired by complexity. Within this framework, exploiting many insights obtained from systems science approach and science cannot be disregarded. This cannot be thought without the presence of computers since the evolution of computing and information processing has been affected by complexity where nature of computers and applications for which computers are utilized are integral components. Regarding modern computing closely tied with complexity theory, many ideas and issues in computing and computational ideas have to be addressed through which it can be possible to fulfill an emerging field in all walks of life to be able to obtain robust and optimized solutions towards more intricate and ever larger problems. From all these broad perspectives, addressing the diverse areas in a numerical methods-based decision-support process based on cutting-edge advances in computational algorithms in the meantime is an encouraging innovation.
Accordingly, the following relevant topical areas can be considered for research and application aspects:
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