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

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Virtual Event

Thursday, September 10, 2020, 4 pm

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This is a past event.

ME-EM Virtual Graduate Seminar Speaker Series

proudly presents:

Yongchao Yang, PhD

Michigan Technological University

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 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. laboratory experiments on a variety of structures and real-world case studies will also be presented.

Bio: Yongchao Yang is an Assistant Professor of Mechanical Engineering at Michigan Tech. 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 30 international journal publications, 3 book chapters, and 2 patents. He was a recipient of a few awards including 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), 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). Before joining Michigan Tech, He was a Staff Scientist at Argonne National Laboratory (2018-2019), after a Director’s Funded Postdoctoral Fellowship at Los Alamos National Laboratory from 2015-2017. He obtained his PhD from Rice University in 2014 and bachelor’s from Harbin Institute of Technology in 2010.

Invited by: Radheshyam Tewari

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