ME Seminar Speaker: Physics-guided Machine Learning Methodology for Full-Field Imaging and Characterization of Structural Dynamics

Thursday, February 07, 2019
2:00 pm to 3:00 pm
Floor/Room #: 
102

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ME Seminar Speaker 


Physics-guided Machine Learning Methodology for Full-Field Imaging and Characterization of Structural Dynamics

Yongchao Yang
​Technical Staff Member
Argonne National Laboratory

​2:00 pm Thursday, February 7th, 2019
​Higgins Labs 102
 


Abstract

Engineering structures and materials usually have complex geometries, material properties, and boundary conditions, and exhibit spatially local, temporally transient, dynamic behaviors. High spatial and temporal resolution vibration measurements and modeling are thus required for high-fidelity characterization, analysis, and prediction of the structure’s dynamic phenomena. However, it is a significant challenge to obtain high-resolution vibration measurements and high-fidelity models using traditional techniques.
In this talk I will present a new full-field imaging method for rapid, high-spatial-temporal-resolution sensing and characterization of structural dynamic behaviors. I will introduce the framework of “computational sensing” through the physics-guided machine learning methodology that enables so. I will demonstrate laboratory experiments on a variety of structures and real-world case studies.

About the Speaker

Yongchao Yang

Yongchao Yang has been a Technical Staff Member at Argonne National Laboratory since 2018, after a Director’s Funded Postdoctoral Fellowship at Los Alamos National Laboratory from 2015-2017. He obtained his Ph.D. from Rice University in 2014 and bachelor’s from Harbin Institute of Technology, China in 2010, both in structural engineering. His expertise is in structural dynamics, experimental mechanics, system identification and health monitoring. His recent research, funded by DARPA and DOE, has focused on developing new high-resolution structural sensing/ imaging and identification methods, combining approaches from computer vision and machine learning. He is the author of more than 20 international journal publications, 3 book chapters, and 2 patents. He was a recipient of the Best Paper Award of the United Nations International Conference on Sustainable Development (New York, 2015), a winner of the TechCrunch Disrupt NY (New York, 2016), mentored a student winning a 2nd place in the student competition of the IEEE Resilience Week (Chicago, 2016), and received the Mary & Richard Mah Publication Prize for Engineering Science (2018), the 2017 Raymond C. Reese Research Prize of American Society of Civil Engineers (ASCE), and an R & D 100 Award (2018).

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