Generative Models for Design and Process Intelligence

Abstract 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.

Bio

Naichen is an assistant professor in the Department of Mechanical Engineering

and the Department of Industrial Engineering and Management Sciences. His

research focuses on integrative data analytics and science-informed AI models.

Naichen received his Ph.D. from the University of Michigan. His work has been

recognized with several honors, including the INFORMS Data Mining Best

Paper Award and the Wilson Prize.