Robert X. Gao Research Group

Case Western Reserve University

How can multi-physics sensing and stochastic modeling improve observability and control of complex manufacturing processes? The group develops multi-physics sensing technologies and miniaturized sensors to measure in-situ process variables such as pressure, temperature, and other signals. Researchers design physics-informed AI and machine learning methods, including physics-guided Gaussian processes and probabilistic recurrent neural networks, to predict system performance and remaining useful life. The lab integrates analytical, numerical, and experimental methods to create high-speed measurement instruments and AI-driven data analytics for process monitoring. Current projects target AI-enhanced control and autonomy in hybrid autonomous manufacturing processes and digital-twin-driven virtual commissioning.