Patil Research Group

Our research spans three interconnected thrusts: design, characterization, and perception of materials and environments through wave-material interactions. Explore selected projects within these thrusts below.

Design

Exploiting interface nonlinearity in continuum for wave propagation control

In this work, we designed and investigated phononic materials with periodic interfaces of rough surfaces. These interfaces exhibit varying degrees of nonlinearity, from weak to strong, including friction. Therefore, we studied how these nonlinearities, along with the unique building block of the layered structure, affect wave propagation. In the weakly nonlinear regime, we 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, we 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, we 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, we showed that the input compression wave disintegrates into several different waveforms, particularly a leading compression pulse, transitional rarefaction front, and tailing oscillations [Patil and Matlack, Euro. Non. Dyn. Conf. (2022)]. Recently, we 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, we 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 next-generation materials and acoustic devices. Ganesh has 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-contact-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]

Characterization

Characterizing geometric architecture to design wave-controlling architected materials

In this work, we developed methods to computationally characterize static and wave propagation responses of 3D lattice materials. Based on this, we designed lattice materials to control elastic waves and enhance material performance for applications like vibration isolation, wave communication, and nondestructive testing. First, we 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 nondestructive evaluation as well as the simultaneous optimization of the mechanical and dynamic properties of architected materials. We then 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, we demonstrated how we can achieve mode conversion and accelerate/decelerate a specific wave. Finally, we 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]

Perception

Echolocation-inspired ultrasound perception using convolutional neural networks

Inspired by the echolocation abilities of bats and dolphins, we developed an ultrasound perception model using convolutional neural networks to interpret acoustic echoes in air. By integrating specialized 1D convolutional neural networks (SCNNs) 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 close proximity 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. Further, we developed a framework for interpreting SCNNs trained on echoic data [Patil et al., Adv. Int. Sys. (2026)]. The method identifies the specific acoustic signatures that each SCNN relies on, quantifies their relative importance in classification, and reveals how predictions emerge from the model’s learned feature hierarchies. 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]
G. U. Patil, H. S. Kwon, B. I. Popa, Interpreting how neural networks infer scatterer geometry from echolocation echoes, Advanced Intelligent Systems, 8, e70434 (2026) [Link]

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