Wei Chen's KEYNOTE seminar "Data-Driven Design of Engineered Materials Systems"

Wei_Chen_May_20_2021.pdf (pdf 186 kB)

Abstract. Design of advanced material systems imposes challenges in integrating knowledge and representation from multiple disciplines and domains such as materials, manufacturing, structural mechanics, and design optimization. Data-driven machine learning and computational design methods provide a seamless integration of predictive materials modeling, manufacturing, and design optimization to enable the accelerated design and deployment of advanced materials systems. In this talk, we will introduce the state-of-the-art data-driven methods for designing heterogeneous nano- and microstructural materials such as polymer nanocomposites, functional microelectronics, and solar cells, as well as complex multiscale metamaterial systems. Research developments in design representation, design evaluation, and design synthesis will be introduced with techniques of microstructure characterization and reconstruction, machine learning, mixed-variable Gaussian process modeling, and Bayesian optimization. Challenges and opportunities in designing engineered material systems will be discussed.