AI-based Metamaterial Design

Interdisciplinary Areas: Data and Engineering Applications

Project Description

This project will explore the development of design tools for elastic engineered material and metastructures leveraging the latest machine learning technologies. Engineered materials 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 near-zero index materials to the more recent class of topological materials. The design space offered by these different types of engineered materials is virtually unbounded and, for the most part, unexplored. The lack of proper design methodologies capable of selectively probing the design space in search of specific material functionalities is one of the major obstacles on the path towards practical applications. Current methods based on physics-driven or numerical (optimization) techniques are largely inadequate to deal with these complex design problems. The postdoctoral student 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, 2024


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
Associate Professor of Civil Engineering
Associate Professor of Electrical and Computer Engineering (courtesy)
Smart Informatix Laboratory

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


Short Bibliography

1. Rih-Teng Wu, Mehdi Jokar, Mohammad R. Jahanshahi and Fabio Semperlotti, (2022), "A physics-constrained deep learning based approach for acoustic inverse scattering problems," Mechanical Systems and Signal Processing, 2022, Vol. 164, 108190, DOI: 10.1016/j.ymssp.2021.108190.
2. Rih-Teng Wu, Ting-Wei Liu, Mohammad R. Jahanshahi, Fabio Semperlotti, (2021), "Design of one-dimensional acoustic metamaterials using machine learning and cell concatenation," Structural and Multidisciplinary Optimization, 2021, Vol: 63, No. 5, 2399–2423, DOI: 10.1007/s00158-020-02819-6..
3. 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.
4. Zhu H., Semperlotti F., "Double-zero-index elastic phononic waveguides", Phys. Rev. Appl., 8, 064031, 2017.
5. Rih-Teng Wu and Mohammad R. Jahanshahi, (2019), "Deep convolutional neural networks for structural dynamic response estimation and system identification," Journal of Engineering Mechanics, American Society of Civil Engineers (ASCE), in press, Vol. 145, No. 1, January 2019, DOI: 10.1061/(ASCE)EM.1943-7889.0001556.