Machine learning for the design of elastic metamaterials

Interdisciplinary Areas: Engineering and Healthcare/Medicine/Biology

Project Description

This project will explore the development of design tools for elastic metamaterials and metastructures leveraging the latest machine learning technologies. Metamaterial systems have opened new, unexpected, and exciting paths for the design of electromagnetic and acoustic devices that only few years ago were considered completely out of reach. Relevant examples range from ultra-flat lenses, to subwavelength imaging beyond the diffraction limit, to the very recent topological state of matter. The design space offered by these different types of engineered materials is virtually unbounded and, for the most part, unexplored. This is due to the lack of proper design methodologies capable of selectively probing the design space in search of specific material functionalities. Current methods based on physics-driven or numerical (optimization) techniques are largely inadequate to deal with these complex design problems.

The postdoctoral researcher will perform theoretical, numerical, and experimental work at the interface between continuum mechanics, wave propagation, and artificial intelligence. The selected candidate will utilize the state of the art facilities at Purdue’s Smart Informatix Laboratory, Structural Health Monitoring and Dynamics Laboratory, and Ray W. Herrick Laboratories in order to perform computations, material fabrication and experiments.

Start Date

August 15, 2019

Postdoc Qualifications

PhD in Mechanical Engineering, Civil Engineering, Aerospace Engineering, or closely related fields. Experience in computational and experimental continuum mechanics, Artificial Intelligence, Machine Learning, dynamics and vibrations is preferred.  


Mohammad R. Jahanshahi
Assistant Professor of Civil Engineering
Smart Informatix Laboratory

Fabio Semperlotti 
Assistant Professor of Mechanical Engineering
Assistant Professor of Aeronautics and Astronautics Engineering (courtesy)
Structural Health Monitoring and Dynamics Laboratory


1. Zhu H., Liu T. W., Semperlotti, F., “Design and experimental observation of valley-Hall edge states in diatomic-graphene-like elastic waveguides”, Phys. Rev. B, 97, 174301, 2018.

2. Zhu H., Semperlotti F., "Double-zero-index elastic phononic waveguides", Phys. Rev. Appl., 8, 064031, 2017.
3. Wu R-T, Jahanshahi M.R., “Deep convolutional neural networks for structural dynamic response estimation and system identification,” Journal of Engineering Mechanics, American Society of Civil Engineers (ASCE), 2018, accepted.
4. Wu R-T, Jahanshahi M.R., “Data fusion approaches for structural health monitoring and system identification: past, present and future,” Structural Health Monitoring, 2018, accepted.