AI-Enabled Experimental Autonomy

Catalog description

(same as CH_ENG 8616 , CHEM 8616 , CMP_SC 8616 , BIOL_EN 8616 , MAE 8616 ). Provides the conceptual tools to implement experimental autonomy in modern materials characterization and analysis. Examines theoretical and practical issues related to employing machine learning techniques to the conventional process - structure - property paradigm of materials science and engineering, with an emphasis on microscale and nanoscale structures. Covers standard machine learning techniques, neural networks and deep learning, data-driven forward and inverse models, strategies and models for autonomous and semi-autonomous materials modeling and design. Graded on A-F basis only. Credit Hour s : 3 Prerequisites: DATA_SCI 7010 , DATA_SCI 8010 , CH_ENG 8615 . Instructor's consent required