Description of the Focus/Specialization Area
As established in inductive mathematical systems theory (for example George Klir's General Systems Problem Solving, Bernard Zeigler's Theory of Modeling and Simulation, or Wayne Wymore's Mathematical Theory of Systems Engineering), Systems research activities (chiefly systems analysis and systems synthesis) can be understood as a movement between layers of abstraction described as distinct system epistemological levels. The key epistemological levels (according to Klir) are the source system (black-box view), the data system (database), the generative system (equation), the structure system (system of equations), and the meta-system (model selector). Other authors proposed comparable taxonomies.
Systems Analysis begins from a pre-existing specification of a system (as theory) and generates data by subjecting this operationalized system specification (typically expressed at the generative and structure levels and implemented in some formalism and programming language) to environmental conditions/scenarios, in order to map the system's state-space at the data system level. Thus, system analysis is an activity that navigates the levels of system epistemology downward, a sort of an expansion of a generative specification into its data equivalent. Systems Design, on the other hand, navigates the epistemological levels upwards, from a black-blox view of the system and a data specification of its functionality, towards a structured and generative specification of the system, a constriction of data into a generative and structural form.
Both the systems research activities above require modeling and simulation. In model-based systems analysis, modeling is descriptive and representational, whereas in model based systems design, modeling is prescriptive/normative. Simulation in systems design serves the purpose of concept validation, in analysis, simulation serves the purpose of data generation, for subsequent analysis (statistical, etc.).
General Expectations
Experts working in this area will need to deploy modeling and simulation skills in a systems design and in a systems analysis context. The contextual element entails that the skill is not just in modeling and simulation techniques (discrete event, agent based, systems dynamics, parallel simulation, VR etc.), but also in the application domains relevant to systems analysis (energy, healthcare, supply chains, etc.) and the foundational knowledge of systems (trade-space explorations, design, resilience, reliability, synthesis, etc.)
Tools of the following nature are of interest for experts working in this area:
- MODELICA
- Mathematica SYSTEMS MODELER
- NetworkX
- SysML/MBSE: CORE, IBM RATIONAL, DOORS, CAMEO SYSTEMS MODELER
- Traditional M&S formalisms: DES, SD, ABM
Courses Emphasized in the Focus Area
Courses supporting modeling and simulation in systems engineering and analysis emphasize modeling and simulation, and analytical skills, particularly as they relate to Systems Engineering Design at both the undergraduate and graduate levels. Examples of existing and new EMSE classes:
- Modeling and Analysis of Systems
- Complex Adaptive System Environments
- Enterprise and Complex System Dynamics
- Agent Directed Simulation and Systems Engineering
- Simulation Based System Engineering Design
Related Research Programs and Information Sources
Several programs have emerged that directly support the research specializing in Modeling and Simulation for Systems Engineering and Analysis. For example:
- NSF recently combined several programs to form the newly established division in Engineering Design and Systems Engineering (EDSE). https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505478
- On a more technical level, Systems Engineering can be closely associate with Engineering Management, and in the case of our department, the new faculty would be expected to have the knowledge and expertise to apply their M&S skill to the design and development of complex organizational structures. https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505202