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Opportunities for Knowledge Management in Model-Based Systems Engineering

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When engineering complex systems, a broad range of information and knowledge needs to be applied. Rather than capturing all this information and knowledge in unstructured text-based documents as used to be common practice in traditional systems engineering approaches, Model-Based Systems Engineering advocates capturing knowledge in semantically rich, structured models. Modeling in this context should be interpreted broadly, encompassing all information and knowledge used for systems engineering: requirements, functions, logical and physical components, logical and physical behavior, etc. With the expanded semantics of these models, the opportunities for automated knowledge capture, application and reuse have increased dramatically. In this presentation, I will provide an overview of capturing knowledge in the Systems Modeling Language (OMG SysML) augmented with domain specific models. Examples focus on the capture and use of synthesis and analysis knowledge for hydraulic systems design.

About the speaker: Dr. Paredis is an Associate Professor in the G.W. Woodruff School of Mechanical Engineering at Georgia Tech. In his research, he combines aspects of information and knowledge management, simulation, and systems theory in support model-based systems engineering, focusing in particular on decision making under uncertainty in conceptual design and architecture definition.

This talk is part of the Engineering Design Centre series.

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