BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH BEGIN:VEVENT UID:20261009T030030-484606922-northwestern.edu DTSTAMP:20261009T030030 DTSTART:20251117T150000 DTEND:20251117T160000 SUMMARY:ME512 Seminar Series- Naichen Shi LOCATION:LR3, Technological Institute DESCRIPTION:Generative Models for Design and Process Intelligence\nAbstract The increasing complexity of modern manufacturing systems calls for new paradigms that tightly integrate data with human expertise and scientific modeling. In this talk, I will present a few recent works that leverage generative models, including diffusion and flow-matching models, to advance design and process intelligence. I will first introduce Coupled Flow Matching, which employs optimal transport and flow-based generative modeling to enable controllable dimension reduction and design. Next, I will discuss the active learning framework enabled by generative diffusion surrogates, which merge prior knowledge with experimental measurements to guide experiment design. I will also introduce our work on diffusion calibration models that combine imperfect computer simulations with real-world measurements. These efforts highlight how generative models can serve as a unifying language to connect data, physics, and humans in the future of manufacturing intelligence.\nBio\nNaichen is an assistant professor in the Department of Mechanical Engineering\nand the Department of Industrial Engineering and Management Sciences. His\nresearch focuses on integrative data analytics and science-informed AI models.\nNaichen received his Ph.D. from the University of Michigan. His work has been\nrecognized with several honors, including the INFORMS Data Mining Best\nPaper Award and the Wilson Prize.\n\nPiP URL: https://planitpurple.northwestern.edu/event/636002 END:VEVENT END:VCALENDAR ORGANIZER:McCormick - Mechanical Engineering (ME)