Moving beyond mere pattern recognition, machines are now capable of extracting new insights from hidden trends and patterns, generating lifelike images and coherent text, and making complex decisions in intricate environments. As these advancements progress at an astonishing pace, it begs the question of whether artificial intelligence (AI) will eventually attain the level of intelligence required to delve into highly intellectual pursuits, such as comprehending the fundamental laws of physics in nature. In this presentation, Dr. Baek will highlight some of the recent frontiers in physics-aware deep learning and demonstrate their application in solving complex mechanical engineering problems, ranging from designing composite materials to predicting quantum spin dynamics.

Dr. Stephen Baek is an applied geometer, engineer, and data scientist with a keen interest in geometric data analysis. He applies computational (differential) geometry, computer vision, and machine learning to address problems in physics, mechanical engineering, materials science, sports science, and medicine. He has been a principal investigator of various research projects funded by the federal government and industry sponsors, such as the NSF, NIH, Air Force, Army, NASA, DOT, Hyundai Motors, etc.