Digital Engineering Graduate Certificate
University of Massachusetts Lowell
Four courses designed to introduce skills in Digital Transformation and
Model-Based Systems Engineering
Project based learning using the Systems Modeling Language (SysML) and Tools such as Cameo, MATLAB/Simulink
Course Descriptions
EECE. 5492 Systems, Modeling and Simulation for Digital Engineering
Introductory course provides a high-level view of systems thinking, systems engineering, physical system modeling, and model-based systems engineering (MBSE). System dynamics will be simulated using platforms such as MATLAB/Simulink. Student will learn to implement MBSE using the systems modeling language (SysML).
System Engineering Overview:
Systems Modeling Language (SysML) Overview:
Example – creating SysML diagrams:
EECE. 5494 Model-Based Systems Engineering
The second course will focus on extending understanding and practice in model-based representation of engineered systems. Systems modeling language (SysML) and MBSE will be used as a primary tool to practice systems thinking. Model-based representation of stakeholder requirements, use-cases and scenarios, and system and interface architecture and behavior will be introduced through case studies.
User Stories to Activity Diagram:
Model Handoff – Concept, Logical, and Physical Models:
EECE. 5496 Cyber-Physical Systems Modeling and Simulation
The focus of the third course is to analyze physical systems and their interactions with embedded digital sub-systems and communication networks in the context of cyber-physical systems (CPS). Continuous and discrete time systems, system control, and state estimation are presented. The specification of functional, behavioral, and security requirements for CPS using a model-based systems engineering framework is undertaken.
CPS System Context:
EECE. 5498 Data-Driven Models. Decision Making and Risk Management
The fourth course in the certificate addresses methodologies for making decisions and managing risk based on data. Data retrieved from models, simulations, or measurements for prediction and inference of estimation of system behavior will be considered. Artificial intelligent (AI) / Machine learning (ML) algorithms, data visualization, and data analytics for making decision, managing risks, and optimizing will be introduced.
