Prerequisites: ENAI600 and ENAI601

This is the equivalent of ENAI605 for Fall 2026 only

This course develops the student s analytical and practical familiarity with, and understanding of, key generative engineering AI techniques and their application across select engineering disciplines. Students will develop proficiency in the mathematical foundations underlying current and future digital twin technologies, including variational filters for inference, generative models for prediction, and reinforcement learning for decision making, and will explore the potential for digital twins todrive innovation and solve complex engineering problems. Through lectures, hands-on assignments, and projects, students will develop knowledge and skills to harness the power of generative AI in advancing engineering research and practice.

The course objective is to learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. Opportunities to use the principles taught in the course arise in all phases of engineering work, including new product design and development, process development, and manufacturing process improvement. Applications from various fields of engineering (including chemical, mechanical, electrical, materials science, industrial, etc.) will be illustrated throughout the course.

Understand organizational decision-making characteristics and the roles of analytics and decision support systems to enhance and optimize outcomes. Through hands-on practice, be able to lead/collaborate to implement a data analytics project to support organizational goals and measure its effectiveness.

This course is aimed at sharing knowledge and experiences in the solar energy and technology area. The goal is to provide students a general summary of the solar energy industries, their technologies, and prospectus in the future alternative energy field. It is also the hope that students will be able to utilize their knowledge in other related fields such as battery and energies.