Mechanical and Civil Engineering Seminar
Mechanical and Civil Engineering Seminar Series
Machine learning in solid mechanics: improving modeling and design
Abstract: Analysis and design of materials & structures can be significantly empowered by machine learning. Yet, there are important barriers to the penetration of machine learning in solid mechanics. This talk walks you through some of the challenges we faced and the proposed solutions that accelerate multi-scale analyses, enable design under uncertainty, and bypass the elephant in the room: data scarcity. Two design examples are provided, a super-compressible metamaterial and a nanomechanical resonator with one of the highest quality factors to date. We end by presenting a new method called Adaptive Self-consistent Clustering Analysis (ASCA) for fast and accurate prediction of plasticity and fracture in representative volume elements of materials.
Bio: Miguel Bessa is an Associate Professor in the Materials Science and Engineering Department at the Delft University of Technology. He is the Director of an inter-faculty Artificial Intelligence lab called MACHINA, dedicated to machine intelligence advances for materials design. He is also the recipient of a Veni personal grant (2019). Prior to coming to the Netherlands, he was a postdoctoral scholar in Aerospace at the California Institute of Technology, and he received his PhD (2016) in Mechanical Engineering at Northwestern University as a Fulbright scholar. He envisions a new era of design of materials and structures through artificial intelligence.
NOTE: At this time, in-person Mechanical and Civil Engineering Lectures are open to all Caltech students/staff/faculty/visitors with a valid Caltech ID. Outside community members are welcome to join our online webinar.
Zoom link: https://caltech.zoom.us/j/82520331395?pwd=bkQ4ZURYOE1WRWxvdmwrcHk0WXN6dz09