Digital Engineering

Graduate Certificate

This course presents critical skills for building information-intensive applications for engineering applications. It teaches ontologies as a formalism to create a common vocabulary for engineering development with precise syntax and logical semantics. Such vocabulary is then used to improve collaboration and communication among engineers and other stakeholders. Moreover, ontologies enable the development of effective engineering-specific solutions with reduced time and effort. Such solutions support modeling, analysis, and review of engineering and programmatic information while enabling federated and collaborative work that can evolve incrementally and be integrated continuously. By mastering these skills, you can tackle real-world problems in your chosen engineering application.

This course presents the foundations of the design and management of digital threads. Students will learn the fundamental characteristics of digital engineering and related approaches such as model-based engineering, before exploring key concepts such as the digital thread and the digital twin. The course will present an approach to capture the need for a digital thread as a use case. Students will then learn the concepts of data interoperability and technical interoperability and will explore the advantages and disadvantages of various approaches that support interoperability. These concepts will include APIs, data standards, data transformations, and ontologies. The course will also present the notion of change management as a crucial consideration of digital thread design. Finally, students will be required to review these considerations in parallel and describe how a digital thread could be deployed in response to a particular problem.

This course delves into the integration of heterogeneous models and simulations to enhance system design, analysis, and lifecycle management. It covers interoperability standards like HLA and TENA, techniques for integrating diverse models, the development and application of digital twins, and the strategic use of federated M&S across the system lifecycle. Students will learn best practices for deploying federated M&S in various industries, preparing them to implement these cutting-edge techniques in real-world engineering projects.

This course provides students with both the conceptual foundations and the hands-on skills needed to understand, evaluate, and apply smart manufacturing technologies. Students will learn about the building blocks of smart systems (digital thread, advanced analytics, intelligent processes, cybersecurity), explore product design and realization in a digital environment, and gain practical experience implementing the industrial machine learning pipeline on real-world datasets (e.g., water treatment systems, metal 3D printing). The course also covers cyber-physical systems cybersecurity challenges and defense strategies, ensuring students appreciate both the opportunities and risks of digital transformation. While the primary focus is on manufacturing, many of the methods and tools introduced generalize to other industrial sectors, enhancing the broader relevance of the course.