Experiences in Evolving System-of-Systems Engineering Methodology to Address Pain Points

被引:0
|
作者
Guariniello, Cesare [1 ]
Raz, Ali K. [1 ]
Mockus, Linas [1 ]
DeLaurentis, Daniel [1 ]
机构
[1] Purdue Univ, Aeronaut & Astronaut, W Lafayette, IN 47907 USA
关键词
System-of-Systems; Artificial Intelligence; Machine Learning; Uncertainty Quantification; Model-Based Systems Engineering; DEPENDENCY ANALYSIS;
D O I
10.1109/sysose.2019.8753852
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The growing interest in System-of-Systems has resulted in rapid and widespread application of its concepts to a variety of problems. While this generated a large number of novel and valuable techniques, tools, and methodologies in System-of-Systems Engineering, it also broadened the problem domains, and subsequently made it difficult to achieve a high degree of coordination and cohesion in the discipline. Previous attempts at creating a formal structure to System-of-Systems Engineering provided valuable support to practitioners, but have been sometimes disconnected from the current practical needs in the discipline, leaving behind some pain points that still need to be addressed. In this work, we propose a different perspective to deal with the pain points: based on our experience and on analysis of the current challenges of System-of-Systems Engineering, we identify potential research directions which are based on domain agnostic methodology, but of interest to a variety of problems in System-of-Systems. This dual feature of the research directions is explored here to identify a connection between the necessity of establishing formal and generic procedures for System-of Systems Engineering along with the usability and utility of practical tools and techniques for applied problems.
引用
收藏
页码:189 / 194
页数:6
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