SE 5000 – Introduction to Systems Engineering
What’s Exciting About this Course? Learning the foundations of systems engineering and gaining an in-depth knowledge of system engineering principles, processes, and methods. Reading about how others apply and excel at Systems Engineering through examples and case studies. Discussing and sharing best practices and challenges with classmates and instructor for building effective systems engineering functions and processes. Applying systems thinking concepts to structured challenges.
Topics: INCOSE SE Vision 2025, Systems Engineering Overview, Life Cycle Stages, Decision Making and Risk Assessment in Design, Model-Based System Engineering, Business and Mission Analysis Process, Stakeholder Needs and Requirements Definition Process, Architecture Definition Process, Interface Design and Definition, System Definition Process, Design Definition Process, System Analysis Process and Implementation Process, Integration, Verification, Transition, and Validation Processes, Operation, Maintenance, Disposal Process, Tailoring SE Processes, Systems Thinking.
SE 5001 – Model-Based Systems Engineering
What’s Exciting About this Course? Applying the knowledge of systems engineering principles, processes, and methods to design cyberphysical systems. Creating architectures, models, and simulations that relate and test all system elements, interfaces, interactions, and performance.
Topics: Creating Requirements, Requirements Modeling, Define the System Context and Boundary, Define Interfaces and External Interface Elements, Define the System Behavior, Advanced System Behavior Modeling, Introduction to Simulating Cyberphysical Systems, Allocate the Behavior to Physical Components, Defining Physical Components, Failure Modes and Effect Analysis (FMEA), Verification Requirements and Test Plans, Integrating and Deploying SysML and MBSE into a Systems Development Environment.
SE 5101 – Acausal Physical Systems Modeling
What’s Exciting About this Course? Developing skills in the areas of fundamental physical and mathematical representations of heat transfer, fluid transport, separations, and their incorporation in large-scale systems. Introducing concepts on how systems can be architected and designed with the aid of models and the basic principles of model-based systems engineering. Understanding the key aspects and advantages of acausal, equation-oriented modeling languages.
Topics: Industry product development processes and Model-Based Systems Engineering principles, Cyber-Physical Systems, Component Modeling, Thermal fluid system models and applications, Large-scale system modeling, Model abstraction and exchange, Mathematical approximations in system modeling, Analogous Models, Systems Thinking, Model Exchange, Modelica, Functional Mockup Interface.
SE 5201 – Embedded/Networked Systems Modeling Abstractions
What’s Exciting About this Course? Familiarize with design flows used in industry for designing, implementing and verifying embedded systems, and learn skills necessary to specify requirements and perform platform-based design, analysis and modeling of embedded and networked systems.
Topics: CTL and LTL Model Checking, Abstract Interpretation, Black-box testing, Switched Systems, Symbolic and Numerical Model Checkers for Timed and Hybrid Systems, Design Flows for Embedded System Design, Implementation & Verification, Embedded Systems Requirements Capture and Architecture Selection, Functional unit modeling methods and tools, software modeling and code generation, real-time architectures and operating systems, distributed system modeling.
SE 5102 – Uncertainty Analysis, Robust Design and Optimization
What’s Exciting About this Course? Learning to quantify uncertainty and design more robust systems accounting for uncertainty in robust decision-making at the design stage.
Topics: Product and Process Development, Optimization, Design of Experiments, Sampling Methods, Uncertainty Analysis, Sensitivity Analysis, Capability Analysis, Dynamic Systems Capability, Robust Design, Reliability, Flexibility, Critical Parameter Management, Root Cause Analysis
SE 5202 – Modern Control Systems
What’s Exciting About this Course? Students learn to design and analyze nonlinear and robust controllers, which apply to a wide range of ubiquitous systems affected by nonlinearity and perturbations. Use of MATLAB for analysis and simulation.
Topics: Root Locus Analysis, Frequency Response Methods, Control Design Using Bode Plots, Closed-loop System Analysis, State-space Models: Basic Properties, State-space Features: Observability and Controllability, Full-state Feedback Control, Open-loop and Closed-loop Estimators, Combined Estimators and Regulators, Linear Quadratic Regulator, Linear Quadratic Estimator and Gaussian, Multivariable and Digital Control Basics, Analysis of Nonlinear Systems.
SE 5302 – Formal Methods
What’s Exciting About this Course? Learning to apply a set of Formal Methods techniques that leads to more reliable design of cyber-physical systems. Engineers can design complex systems that result in fewer deviations from the intended and expected behavior of the system.
Topics: Classical Results in Computer Science: Propositional and Predicate Logic, Floyd-Hoare logic, CTL and LTL Model Checking, Abstract Interpretation, SAT and SMT Solvers, Black-box testing. Classical Results in Control Theory: PID Controls, State space control techniques, Linear and Nonlinear Controls, Lyapunov and Inverse Lyapunov functions, Switched Systems. Recent Research in CPS Verification: Symbolic and Numerical Model Checkers for Timed and Hybrid Systems. Applications: Air-traffic Control Protocols, Automotive Control Systems, Robotics, Analog Circuits, Stabilizing Switched Systems, Power-grid systems.
SE 5095 – Machine Learning for Physical Science
What’s Exciting About this Course? Scientific machine learning is a rapidly growing area of research and development, with machine learning starting to play a role in everything from aerospace to battery design. With this exciting interdisciplinary field as context, this course will address key concepts in applied machine learning and discuss challenges and opportunities for future innovation.
Topics: Sample complexity, active learning, transfer learning, noisy data, imbalanced data, feature engineering, feature selection, dimensionality reduction, representation learning, generative adversarial networks, time series, model selection, model assessment, stability, interpretability, meta-learning. Applications include structure-property relationships for molecules and materials; computer vision and scientific imaging; molecular dynamics and turbulence modeling.
SE 5402 – Architecture of IoT
What’s Exciting About this Course? Applying emerging wired and wireless networking protocols, real-time and embedded systems design principles, and edge and cloud computing technologies to design and develop Internet of Things (IoT) applications, and evaluate its performance. Understanding the constraints, requirements, and architectures of hardware and software components for IoT systems.
Topics: IoT System Examples, Architectural Design of IoT Solutions, Popular Embedded Platforms for IoT, CC2650 SoC as a Case Study, Spectrum Allocation, Noise and Interference, Suppression, NI USRP Platform, AD Pluto Platform, GNU Radio, Low-Power Wireless MAC Layer Design: ZigBee (802.15.4), Bluetooth, 6TiSCH, NB-IoT, High-Speed Wireless MAC Layer Design: IEEE 802.11 Families, Cellular Concept, Evolution from 1G to 5G, FDMA, TDMA, CDMA, OFDMA, SDMA, Narrowband IoT, Protocol Compression, 6LoWPAN Adaptation Layer, RPL Routing Protocols, Popular IoT Gateway platforms, Edge Learning, Real-Time Parallel Data Processing Engine, Distributed No-SQL DB
SE 5502 – Capstone Project in Systems Engineering
What’s Exciting About this Course? This course provides the opportunity for students to synthesize and apply the complex and various aspects of systems engineering acquired throughout their program of study to a real-life project or problem of their choosing. Ultimately, a capstone project can represent new work and ideas, and give students the opportunity to demonstrate the knowledge and skills gained during the program.
SE 5702 -Data Science for Materials and Manufacturing
What’s Exciting About this Course? The students will learn data mining and machine learning methods for materials and manufacturing-related applications. This course provides you a project-based learning experience. The students will apply data mining and machine learning techniques to tackle challenges in their research or projects.
Topics: Introduction to manufacturing processes, Principles of Design for Manufacturing (DFM), Design of Experiment(DOE) and data collection, Data visualization, Optimization and regression, Supervised learning methods, Unsupervised learning methods, Ensemble modeling, Applications of data analytics in manufacturing, Application of data analytics in computational materials engineering.
Masters Core Courses (9 Credits)