scholarly journals MBSE with/out Simulation: State of the Art and Way Forward

Systems ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 40 ◽  
Author(s):  
Bernard Zeigler ◽  
Saurabh Mittal ◽  
Mamadou Traore
Keyword(s):  
Complex Systems ◽  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Engineering Application ◽  
Systems Of Systems ◽  
Integrated Simulation ◽  
Model Based ◽  
Complex Adaptive ◽  
Emergency Ambulance

The limitations of model-based support for engineering complex systems include limited capability to develop multifaceted models as well as their analysis with robust reliable simulation engines. Lack of such Modeling and Simulation (M&S) infrastructure leads to knowledge gaps in engineering such complex systems and these gaps appear as epistemological emergent behaviors. In response, an initiative is underway to bring Model-Based Systems Engineering (MBSE) closer together with model-based simulation developments. M&S represents a core capability and is needed to address today’s complex, adaptive, systems of systems engineering challenges. This paper considers the problems raised by MBSE taken as a modeling activity without the support of full strength integrated simulation capability and the potential for, and possible forms of, closer integration between the two streams. An example of a system engineering application, an unmanned vehicle fleet providing emergency ambulance service, is examined as an application of the kind of multifaceted M&S methodology required to effectively deal with such systems.

Emerging interdisciplinary fields in the coming intelligence/convergence era

2012 ◽  
Vol 2 (3) ◽  
Author(s):  
Ahmed Noor
Keyword(s):  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Engineering ◽  
Smart Cities ◽  
Transportation Systems ◽  
Engineering Practice ◽  
Systems Of Systems ◽  
Complex Adaptive ◽  
Definition Of

AbstractDramatic advances are in the horizon resulting from rapid pace of development of several technologies, including, computing, communication, mobile, robotic, and interactive technologies. These advances, along with the trend towards convergence of traditional engineering disciplines with physical, life and other science disciplines will result in the development of new interdisciplinary fields, as well as in new paradigms for engineering practice in the coming intelligence/convergence era (post-information age). The interdisciplinary fields include Cyber Engineering, Living Systems Engineering, Biomechatronics/Robotics Engineering, Knowledge Engineering, Emergent/Complexity Engineering, and Multiscale Systems engineering.The paper identifies some of the characteristics of the intelligence/convergence era, gives broad definition of convergence, describes some of the emerging interdisciplinary fields, and lists some of the academic and other organizations working in these disciplines. The need is described for establishing a Hierarchical Cyber-Physical Ecosystem for facilitating interdisciplinary collaborations, and accelerating development of skilled workforce in the new fields. The major components of the ecosystem are listed.The new interdisciplinary fields will yield critical advances in engineering practice, and help in addressing future challenges in broad array of sectors, from manufacturing to energy, transportation, climate, and healthcare. They will also enable building large future complex adaptive systems-of-systems, such as intelligent multimodal transportation systems, optimized multi-energy systems, intelligent disaster prevention systems, and smart cities.

A Complex Adaptive Systems Engineering Methodology

Insight ◽  
2021 ◽  
Vol 24 (2) ◽  
pp. 25-31
Author(s):  
Brian E. White ◽  
Mickael Bouyaud
Keyword(s):  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complex Adaptive

Complex Adaptive Systems Thinking Approach to Enterprise Architecture

2016 ◽  
pp. 339-389
Author(s):  
Marc Rabaey
Keyword(s):  
Complex Systems ◽  
Systems Thinking ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Enterprise Architecture ◽  
Time Dynamic ◽  
Complex Adaptive ◽  
Unstable Environment ◽  
Systems Thinking Approach ◽  
High Degree

Complex systems interact with an environment where a high degree of uncertainty exists. To reduce uncertainty, enterprises (should) create intelligence. This chapter shows that intelligence has two purposes: first, to increase and to assess (thus to correct) existing knowledge, and second, to support decision making by reducing uncertainty. The chapter discusses complex adaptive systems. Enterprises are not only complex systems; they are also most of the time dynamic because they have to adapt their goals, means, and structure to survive in the fast evolving (and thus unstable) environment. Crucial for enterprises is to know the context/ecology in which they act and operate. The Cynefin framework makes the organization and/or its parts aware of the possible contexts of the organization and/or its parts: simple, complicated, complex, chaotic, or disordered. It is crucial for the success of implementing and using EA that EA is adapted to function in an environment of perpetual change. To realize this, the chapter proposes and elaborates a new concept of EA, namely Complex Adaptive Systems Thinking – Enterprise Architecture (CAST-EA).

Modularity and Complex Adaptive Systems

2008 ◽  
pp. 75-104
Author(s):  
David Cornforth ◽  
David G. Green
Keyword(s):  
Fault Tolerance ◽  
Complex Systems ◽  
Long Range ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Single Unit ◽  
Phase Changes ◽  
Critical Phase ◽  
Complex Adaptive ◽  
Different Levels

Modularity is ubiquitous in complex adaptive systems. Modules are clusters of components that interact with their environment as a single unit. They provide the most widespread means of coping with complexity, in both natural and artificial systems. When modules occur at several different levels, they form a hierarchy. The effects of modules and hierarchies can be understood using network theory, which makes predictions about certain properties of systems such as the effects of critical phase changes in connectivity. Modular and hierarchic structures simplify complex systems by reducing long-range connections, thus constraining groups of components to act as a single component. In both plants and animals, the organisation of development includes modules, such as branches and organs. In artificial systems, modularity is used to simplify design, provide fault tolerance, and solve difficult problems by decomposition.

scholarly journals Complex Adaptive Systems of Systems (CASOS) engineering environment.

2012 ◽  
Author(s):  
Richard Joseph Detry ◽  
John Michael Linebarger ◽  
Patrick D. Finley ◽  
S. Louise Maffitt ◽  
Robert John, Jr. Glass ◽  
...  
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Systems Of Systems ◽  
Complex Adaptive ◽  
Engineering Environment

1. Complex systems

2014 ◽  
Author(s):  
John H. Holland
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Tropical Rainforest ◽  
Hierarchical Organization ◽  
Self Organization ◽  
Physical Systems ◽  
Complex Adaptive ◽  
Distinctive Property ◽  
Adaptive Interaction

What is complexity? A complex system, such as a tropical rainforest, is a tangled web of interactions and exhibits a distinctive property called ‘emergence’, roughly described by ‘the action of the whole is more than the sum of the actions of the parts’. This chapter explains that the interactions of interest are non-linear and thus hierarchical organization is closely tied to emergence. Complex systems explains several kinds of telltale behaviour: emergent behaviour, self-organization, chaotic behaviour, ‘fat-tailed behaviour’, and adaptive interaction. The field of complexity studies has split into two subfields that examine two different kinds of emergence: complex physical systems and complex adaptive systems.

scholarly journals The World is More Than Complicated

2011 ◽  
Vol 133 (11) ◽  
pp. 30-35
Author(s):  
Ahmed K. Noor
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Emergent Behavior ◽  
Global Response ◽  
Emergent Complexity ◽  
Engineering Designs ◽  
Complex Adaptive ◽  
Engineered Systems ◽  
The Individual

This article discusses the need of complex systems to be adaptive, and various innovative technologies that are required to engineer these systems. Complex adaptive systems consist of several simultaneously interacting parts or components, which are expected to function in an uncertain, complex environment, and to adapt to unforeseeable contingencies. The defining characteristics of complex adaptive systems are that the components are continually changing, the systems involve many interactions among components, and configurations cannot be fully determined in advance. Studies have shown that complex systems of the future will require a multidisciplinary framework—an approach that has been called emergent (complexity) engineering. Emergent engineering designs a system from the bottom-up by designing the individual components and their interactions that can lead to a desired global response. Although significant effort has been devoted to understanding complexity in natural and engineered systems, the research into complex adaptive systems is fragmented and is largely focused on specific examples. In order to accelerate the development of future diverse complex systems, there is a profound need for developing the new multidisciplinary framework of emergent engineering, along with associated systematic approaches, and generally valid methods and tools for high-fidelity simulations of the collective emergent behavior of these systems.

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