For decades, much of engineering research has focused on developing models and simulations based on physics and real-world properties that would produce data to confirm or reject a hypothesis. More recently, many engineering scholars have begun utilizing machine learning which relies on vast quantities of data and algorithms to eventually produce a hypothesis. Now, the United States Department of Education has awarded a $1 million-grant to Ching-Long Lin, Edward M. Mielnik and Samuel R. Harding Professor and chair of the Department of Mechanical Engineering at the University of Iowa, to develop artificial intelligence, modeling and simulation (AIMS) programs that will bridge the gap between these two research approaches.
“As we train the next generation of engineers, we want to ensure that they have the full suite of research tools available to them,” said Lin. “By integrating modeling and simulation work with machine learning, we can apply the physical principles that are central to modeling and simulation with smart, intelligent machines that do not have access to real-world interactions.”
This integration will result in a physics-informed neural network which will use physical principles in the machine learning process. The hybrid research approach will be incorporated into existing MS and PhD coursework in the Department of Mechanical Engineering, enhancing professional preparation in areas such as product design, computer aided engineering, and propulsion engineering. In addition to enriching existing courses, the grant will support certificate programs for students transitioning directly from undergraduate to graduate programs. Other activities associated with this grant might include workshops and hackathons, examples of activate learning that have been successful in variety of engineering disciplines.
“We are excited to create these programs to both modernize our existing courses as well as offer new opportunities for certificate completion,” said Lin. “We expect that this new approach will help recruit new students who want to combine physical and data-centric approaches.”
Lin’s team on the grant includes: Sharif Rahman and Jia Lu, UI professors of mechanical engineering; Shaoping Xiao, a UI associate professor of mechanical engineering; Rachel Vitali, a UI assistant professor of mechanical engineering; and Jane Russell, director of research and analytics in the UI Office of Teaching, Learning, and Technology.
For more information on the award, please visit the Department of Education project website.