Digital Engineering Graduate Certificate
Compose of four successive courses offered in the Department of Electrical and Computer Engineering
- These courses implement a project based learning where students will take an initiative to solve real-world problems
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 MATLAB, Simulink, and Python programming platforms. 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 a model-based graphical representation of engineered systems. Systems modeling language (SysML) and MBSE will be used as a primary tool to practice in 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 called 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 final course of the certificate will address the analysis of decisions driven by 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 also introduced .