Ganesh Patil's Research Webpage

My research explores how mechanical waves travel through media across different scales (micro to meso to macro). With this understanding, I aim to discover new ways to control waves, either to prevent damage, amplify useful effects, or extract valuable information. Specifically, my current research focus is on two fronts:

Phononic Materials and Metamaterials

I engineer materials by leveraging geometry and architected nonlinearity to enable wave propagation properties not found in natural materials.

Ultrasound Perception

I develop artificial echolocation models for ultrasound-based perception by investigating biosonars and integrating AI/ML models

Research Projects:

Lattice Metamaterials

Exploiting geometric architecture to control wave propagation

In this work, I designed and computationally analyzed 3D lattice materials to control elastic waves and enhance material performance for applications like vibration isolation, wave communication, and nondestructive testing. I developed a generalized Bloch-wave homogenization approach to predict the mechanical properties of both auxetic (expanding under stretch) and non-auxetic lattices from wave propagation data [Patil and Matlack, J. Acoust. Soc. Am. (2019)]. This method may be useful in the nondestructive evaluation as well as the simultaneous optimization of the mechanical and dynamic properties of architected materials. I also engineered auxetic lattices to make shear waves propagate faster than pressure waves, which is extremely rare in natural materials [Patil et al., Appl. Phys. Lett. (2019)]. Through this unique control, I demonstrated how we can achieve mode conversion and accelerate/decelerate a specific wave. Finally, I created multifunctional lattices that combine vibration isolation with high thermal conductivity by incorporating heat pipes into hollow trusses [Babatola et al., Adv. Engg. Mat. (2020)]. These materials could protect structures from vibrations while improving thermal management in electronics or aerospace components. Read the publications below for more information:

O. Babatola*, G. U. Patil*, D. Hsieh*, K. H. Matlack, S. Sinha, Independently tunable thermal conductance and phononic bandgaps of 3D lattice materials. Advanced Engineering Materials, 22(2), 1901004 (2019). [Link] (*joint first authors)
G. U. Patil, A.S. Shedge, K. H. Matlack, 3D auxetic lattice materials for anomalous elastic wave polarization. Applied Physics Letters, 115(9), 091902 (2019). [Link]
G. U. Patil, K. H. Matlack, Effective property evaluation and analysis of three-dimensional periodic lattices and composites through Bloch-wave homogenization. The Journal of the Acoustical Society of America, 145(3), 1259–1269 (2019). [Link]

Nonlinear Phononic Materials

Exploiting interface nonlinearity in continuum for wave propagation control

In this work, I studied phononic material with periodic interfaces of rough surfaces. These interfaces exhibit varying degrees of nonlinearity, from weak to strong, including friction. Therefore, I studied how these nonlinearities, along with the unique building block of the layered structure, affect wave propagation. In the weakly nonlinear regime, I showed that these materials give rise to interesting properties such as harmonic generation, spatial beating of harmonic amplitudes, and filtering of generated frequencies [Patil and Matlack, Wave Motion (2021)]. Using these signatures, I showed how wave mixing inside these materials can be leveraged to design a phononic diode that can enable broadband nonreciprocity [Patil et al., Ext. Mech. Lett. (2022)]. Under strong nonlinearity, I found that these materials support a rare type of solitary waves - stegotons - that attenuate at certain frequencies due to acoustic resonances of elastic layers [Patil and Matlack, Phy. Rev. E (2022)]. For sufficiently large amplitudes, I showed that input compression wave disintegrates into several different waveforms, particularly, leading compression pulse, transitional rarefaction front, and tailing oscillations [Patil and Matlack, Euro. Non. Dyn. Conf. (2022)]. Recently, I reported the emergence of eigenstrains (residual static shear deformations) when shear waves interact with rough interfaces [Patil et al., J. App. Mech. (2023)]. By leveraging these deformations, I demonstrated wave-governed programmable functionalities such as mechanical switches, precision actuation, and surface reconfigurability. All these wave properties with no analogs in linear theory could potentially lead to the next-generation materials and acoustic devices. I have also written an extensive review paper that provides perspectives on how different sources of nonlinearity can be used in phononic media to enable enriched wave dynamics [Patil and Matlack, Acta Mechanica (2022)]. Read the publications below for more information:

- - G. U. Patil, A. Fantetti, K. H. Matlack, Shear wave-induced friction at periodic interfaces for programmable mechanical responses. Journal of Applied Mechanics, 90(9), 091002 (2023). [Link]
  - G. U. Patil, K.H. Matlack, Nonlinear Wave Disintegration in Phononic Material with Weakly Compressed Rough Contacts. 10th European Nonlinear Dynamics Conference - Lyon, 382267 (2022). [Link]
  - G. U. Patil, Songyuan Cui, K. H. Matlack, Leveraging nonlinear wave mixing in rough contacts-based phononic diodes for tunable nonreciprocal waves. Extreme Mechanics Letters, 55, 101821 (2022). [Link]
  - G. U. Patil, K. H. Matlack, Strongly nonlinear wave dynamics of continuum phononic materials with periodic rough contacts. Physical Review E, 105(2), 024201 (2022). [Link]
  - G. U. Patil, K. H. Matlack, Review of exploiting nonlinearity in phononic materials to enable nonlinear wave responses. Acta Mechanica, 233(1), 1-46 (2021). [Link]
  - G. U. Patil, K. H. Matlack, Wave self-interactions in continuum phononic materials with periodic contact nonlinearity. Wave Motion, 105, 102763 (2021). [Link]

Ultrasound Perception

Echolocation-inspired ultrasound perception using convolutional neural networks

Inspired by the echolocation abilities of bats and dolphins, I developed an ultrasound perception model using convolutional neural networks to interpret acoustic echoes in air. By integrating specialized 1D convolutional neural networks with physics-based data enhancements, the model accurately identifies object shapes from echoes alone and processes real-world data despite being trained only on synthetic datasets [Patil et al., J. Sound Vib. (2025)]. Further, this model is scalable as it can learn new shapes sequentially, it can identify objects when two objects are in the close vicinity, and can also localize them [Kwon et al., IEEE Access (2025)]. This approach eliminates the need for costly and time-consuming data collection, making it efficient for applications like nondestructive testing, sonar navigation, and autonomous robotics. Read the publications below for more information:

G. U. Patil, H. S. Kwon, B. I. Epureanu, B. I. Popa, Synthetically-trained neural networks for shape classification from measured acoustic scattering, Journal of Sound and Vibration, 618, 119229 (2025) [Link]
H. S. Kwon, G. U. Patil, B. I. Epureanu, B. I. Popa, Specialized convolutional neural network models for echolocation-based perception, IEEE Access, 13, 156811-158623 (2025) [Link]

Research Impact: