Digital Twins and Analytics

Catalog description

Digital Twins and Analytics is a cross-listed (graduate/undergraduate) course designed to equip students with the knowledge and skills necessary to understand, implement and integrate digital twin technologies for dynamic systems. The course integrates theoretical foundations with practical insights from industry, focusing on how Digital Twins are applied across multinational, transnational, and global organizations. This course will equip students with the knowledge and skills necessary to build digital twins that go beyond traditional modeling and simulation, enabling them to integrate data, AI, and real-world applications for creating functional, scalable, and impactful digital twin solutions. This course provides a fundamental understanding of digital twins, beginning with an introduction to the concept, including their relationship to physical systems and virtual models. The course also provides an overview of the integration of digital twins, addressing fundamental principles, technical challenges, and the importance of data acquisition, preparation, and management. The course highlights the role of digital marketplaces and ecosystems in enabling accessibility, as well as the critical synergy between artificial intelligence and digital twins. Topics include hybrid approaches that combine knowledge and data, deployment opportunities and challenges, and the importance of best practices and standards. Applications are explored across industries such as healthcare, energy, smart manufacturing, and advanced electronics packaging, with attention to sustainability and future trends. In addition to theoretical foundations, students engage in a course project, receive insights from guest lectures and industry experts, and develop practical perspectives on leveraging digital twins in real-world systems. Prerequisites: SSIE520 Modeling and Simulation or equivalent. Offered every Fall and Spring semester as needed.