Data Science in Mechanical Engineering
Data Science in Mechanical Engineering is becoming increasingly critical across various domains within the field, integrating artificial intelligence, machine learning, and data-driven approaches. This integration is transforming traditional engineering approaches, aiming to surpass the capabilities of human modeling, design, and decision-making. Through data-driven optimization, aircraft designs become safer and more efficient, while AI-enabled models and simulations offer accurate predictions of complex mechanical systems. Robotics gain enhanced autonomy and control, and the implementation of digital twins is transforming manufacturing processes. This shift towards data science within mechanical engineering is fostering a significant transformation, enhancing the precision and efficiency of engineering solutions.
Associated Faculty: Prosenjit Bagchi, Xiaoli Bai, Steven Berg, Laurent Burlion, Xi Gu, Yuebin Guo, Zhixiong (James) Guo, Yogesh Jaluria, Rajiv Malhotra, Assimina A. Pelegri, Amin Reihani, Nikolaos Napoleon Vlassis, Jingang Yi
Research Clusters
Machine Learning in Material Modeling, Design, and Characterization
Faculty: Yuebin Guo, Rajiv Malhotra, Assimina A. Pelegri, Nikolaos Napoleon Vlassis, Jingang Yi
Machine Learning in Thermal Sciences and Processes
Faculty: Zhixiong (James) Guo, Yogesh Jaluria, Amin Reihani
AI-Enabled Robotic Manufacturing
Faculty: Yuebin Guo, Rajiv Malhotra
AI-enhanced Computational Modeling and Simulation
Faculty: Prosenjit Bagchi, Xiaoli Bai, Laurent Burlion, Zhixiong Guo, Yuebin Guo, Yogesh Jaluria, Rajiv Malhotra, Assimina A. Pelegri, Nikolaos Napoleon Vlassis, Jingang Yi
High-throughput Experiments for Data Collection
Faculty: Steven Berg
AI in Space Exploration and Robotics
Faculty: Xiaoli Bai, Laurent Burlion, Jingang Yi
Machine Learning for Engineering Systems Optimization
Faculty: Xiaoli Bai, Laurent Burlion, Xi Gu, Zhixiong (James) Guo, Yuebin Guo, Yogesh Jaluria, Rajiv Malhotra, Assimina A. Pelegri, Amin Reihani, Jingang Yi