Data-Driven Computational Design of Engineered Material Systems

Explore the cutting-edge field of data-driven computational design for engineered material systems in this 49-minute seminar presented by Wei Chen from Northwestern University. Delve into the challenges of integrating knowledge from multiple disciplines such as materials science, manufacturing, structural mechanics, and design optimization. Learn about state-of-the-art data-driven methods for designing heterogeneous nano- and microstructural materials and complex multiscale metamaterial systems. Discover research developments in design representation, evaluation, and synthesis, along with innovative design methods that combine machine learning, mixed-variable Gaussian process modeling, Bayesian optimization, topology optimization, and digital twin concepts. Gain insights into the challenges and opportunities involved in designing advanced material systems, and understand how data-driven machine learning and computational design methods enable accelerated design and deployment of these systems.