Grainger College of Engineering Seminars & Speakers

Abstract: World models that recreate and simulate the physical world hold transformative potential across robotics, entertainment, and engineering analysis. Achieving this vision requires both generating 3D environments from limited observations and predicting how they evolve under physical actions. Pure physical modeling provides guarantees and action control but demands complete state specification rarely available in practice; pure generative learning handles incomplete information to produce realistic content but lacks the structured representations needed for physical interaction and reasoning. This talk presents physics-grounded world models, integrating these approaches to leverage their complementary strengths: physical representations provide the structured interface for actions and consistency guarantees, while generative models supply visual realism and compensate for incomplete observations. I will demonstrate this framework across two core capabilities---generating 3D worlds from single images and simulating dynamics under physical actions---and show how it extends to real engineering problems in fluid and thermal analysis.

Speaker Bio.: Hong-Xing (Koven) Yu (https://kovenyu.com/) is a PhD candidate at the Computer Science Department of Stanford University, advised by Prof. Jiajun Wu. His research focuses on physics-grounded world models. He is a recipient of the SIGGRAPH Asia Best Paper Award, the Stanford SoE Fellowship, the Qualcomm Fellowship, and the Meshy Fellowship, and a finalist of the NVIDIA Fellowship, the Meta Fellowship, the Jane Street Fellowship, and the Roblox Fellowship.