Data-Driven Computational Design of Engineered Material Systems

Wilson-Cook Prof. Wei CHEN, Northwestern University, USA

14:00-15:00,  August 7, 2023

Yiucheng Lecture Hall (500), Xu Zuyao Building

Biography

Dr. Wei Chen is the Wilson-Cook Professor in Engineering Design and Chair of Department of Mechanical Engineering at Northwestern University. Directing the Integrated DEsign Automation Laboratory (IDEAL- http://ideal.mech.northwestern.edu/), her current research involves the use of statistical inference, machine learning, and uncertainty quantification techniques for design of emerging materials systems including microstructural materials, metamaterials and programmable materials. She serves as the Design Thrust lead for the newly funded NSF Engineering Research Center (ERC) on Hybrid Autonomous Manufacturing, Moving from Evolution to Revolution (HAMMER), where she works on digital twin systems for concurrent materials and manufacturing process design. Dr. Chen is an elected member of the National Academy of Engineering (NAE). She served as the Editor-in-chief of the ASME Journal of Mechanical Design, the Chair of the ASME Design Engineering Division (DED), and the President of the International Society of Structural and Multidisciplinary Optimization (ISSMO). Dr. Chen is the recipient of the 2022 Engineering Science Medal from the Society of Engineering Science (SES), ASME Pi Tau Sigma Charles Russ Richards Memorial Award (2021), ASME Design Automation Award (2015), Intelligent Optimal Design Prize (2005), ASME Pi Tau Sigma Gold Medal achievement award (1998), and the NSF Faculty Career Award (1996).  She received her Ph.D. from the Georgia Institute of Technology in 1995. 

Abstract

Designing advanced material systems poses 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, enabling the accelerated design and deployment of advanced material systems. In this talk, we will introduce state-of-the-art data-driven methods for designing heterogeneous nano- and microstructural materials and complex multiscale metamaterial systems. We will discuss research developments in design representation, design evaluation, and design synthesis, along with novel design methods that integrate machine learning, mixed-variable Gaussian process modeling, Bayesian optimization, topology optimization, and the concept of digital twins. Furthermore, we will address the challenges and opportunities involved in designing engineered material systems.