System theoretic foundations of modeling and simulation: a historic perspective and the legacy of A Wayne Wymore

Ören, Tuncer I.; Zeigler, Bernard P.

doi:10.1177/0037549712450360

Cited by 32 publications

(17 citation statements)

References 22 publications

(12 reference statements)

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“…As related by Ö ren and Zeigler (2012), Ö ren received his Ph.D. in systems engineering under the supervision of A.W. Wymore.…”

Section: Some Related Historymentioning

confidence: 93%

“…Tuncer Ö ren has developed a list of coupling types and associated terms (Ö ren 2014). It details structural input/output relationships with types for coupled models with considerations such as dynamic changes in input/ output couplings and model parts that can be defined using the system-theoretic approach (Wymore 1993;Ö ren and Zeigler 2012).…”

Section: Introductionmentioning

confidence: 99%

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Concepts and Methodologies for Modeling and Simulation

Yılmaz¹

2015

Simulation Foundations, Methods and Applications

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“…As related by Ö ren and Zeigler (2012), Ö ren received his Ph.D. in systems engineering under the supervision of A.W. Wymore.…”

Section: Some Related Historymentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Concepts and Methodologies for Modeling and Simulation

Yılmaz¹

2015

Simulation Foundations, Methods and Applications

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“…22 The system theory-based approach to modeling and simulation has resulted in many enhancements in the field, one of which is SES. 35 It enables compact specification of a family of models utilizing interactions of decomposition, coupling and taxonomies. 36 SES is also defined as a formal ontology framework to specify the elements of a system and their relations in hierarchical fashion.…”

Section: System Entity Structurementioning

confidence: 99%

Extending the Knowledge Discovery Metamodel for architecture-driven simulation modernization

Durak

2015

SIMULATION

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With the rapid improvement of hardware, the constant evaluation of simulation development methodologies and environments has brought us a new challenge: simulations modernization. This paper presents a simulation knowledge discovery metamodel that extends the Object Management Group's knowledge discovery metamodel by adding a simulation model package for enabling architecture-driven simulation modernization. While there are some endeavors that propose integration methods, modernization of legacy simulations has not been investigated. Architecture-driven modernization has been introduced as a process of comprehending and transforming existing software assets. It advocates a modelbased approach to software modernization in which the knowledge extracted from software assets is captured in models that conform to a metamodel, namely the knowledge discovery metamodel. It specifies an ontology of software assets. Model transformations are then recommended as means of modernization of legacy assets. But diversity in methodologies and approaches to specify simulation modeling assets prohibits the Knowledge Discovery Metamodel from providing adequate meta definitions to capture knowledge in simulations. System Entity Structure has long been used for knowledge representation by the simulation community. It provides formalism for composition, taxonomy and coupling relations. Hence, System Entity Structure is proposed as an intermediate metamodel to capture the meta-constructs and their relations. It is then made available to define particular metamodels for particular simulation modeling methodologies or approaches. The promoted methodology is exercised with samples in order to collect evidence of its applicability. Metamodel segments are presented for knowledge discovery for discrete event systems specification and Modelica simulation programming language. A complete metamodel is then introduced for the real-time distributed simulation domain model of the German Aerospace Center (DLR) Institute of Flight Systems. Finally, this metamodel is used to introduce discovery artifacts. These samples provide valuable indications that the methodology works.

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“…Furthermore, DEVS enables fundamental emergence modeling because it operationalizes the closure-under-coupling conditions that form the basis of well-defined resultants of system composition especially where feedback coupling prevails [7,8].…”

Section: Introductionmentioning

confidence: 99%

Emergence at the Fundamental Systems Level: Existence Conditions for Iterative Specifications

Zeigler¹,

Muzy

2016

Systems

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Abstract:Conditions under which compositions of component systems form a well-defined system-of-systems are here formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: (1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state; and (2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and, if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

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System theoretic foundations of modeling and simulation: a historic perspective and the legacy of A Wayne Wymore

Cited by 32 publications

References 22 publications

Concepts and Methodologies for Modeling and Simulation

Concepts and Methodologies for Modeling and Simulation

Extending the Knowledge Discovery Metamodel for architecture-driven simulation modernization

Emergence at the Fundamental Systems Level: Existence Conditions for Iterative Specifications

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