Tunable nonlocal purely active nonreciprocal acoustic media

Geib, Nathan; Sasmal, Aritra; Wang, Zhuzhu; Zhai, Yali; Popa, Bogdan-Ioan; Grosh, Karl

doi:10.1103/physrevb.103.165427

Cited by 24 publications

(6 citation statements)

References 25 publications

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“…Metamaterial design validation is typically done by fabricating small prototypes whose dynamic behavior is measured in a series of experiments in which waveguides are used to mitigate the influence of edge diffraction on the scattered fields 9 . These methods involve placing the sample under test inside carefully designed waveguides and inverting reflection and transmission coefficients to obtain material properties such as dynamic mass density and various elastic moduli [10][11][12][13][14] . However, these methods are either unsuitable in applications in which the sample cannot be manipulated (e.g., in non-invasive diagnostics, imaging) or cumbersome to apply (e.g., for materials operating in aqueous environments in which the waveguide itself contaminates the scattered fields) 15 .…”

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confidence: 99%

Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks

et al. 2022

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Conventional methods used to identify the dynamical properties of unknown media from scattered mechanical waves rely on analytical or numerical manipulations of the wave equation. These methods show their limitations in scenarios where the analyzed medium is moderately sized and the diffraction from the material edges influences the scattered fields significantly, such as non-destructive diagnostics and metamaterial characterization. Here, we show that convolutional neural networks can interpret the diffracted fields and learn the mapping between the scattered fields and all the effective material parameters including mass density and stiffness tensors from a small set of numerical simulations. Furthermore, networks trained with synthetic data can process physical measurements and are very robust to measurement errors. More importantly, the trained network provides insight into the dynamic behavior of matter including quantitative measures of the scattered field sensitivity to each material property and how the sensitivity changes depending on the material under test.

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confidence: 99%

Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks

et al. 2022

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“…Some studies showed that by using a direct voltage applied to the membrane, the eigen-frequencies of AMs could be tuned, and therefore, the wave transmission phase could be tuned as well (Kumar and Lee, 2019). In the case of an alternating voltage applied to the membrane, the vibrations of the membrane AMs resulting from such excitation could be greatly enhanced or suppressed, which could help achieve a variety of functionalities such as acoustic switches with high on/off ratios and phase modulations (Xiao et al, 2015), changing the effective modulus (Li et al, 2019c), as an anomalous acoustic reflector (Zhai et al, 2021), for steerable reflections over a wide frequency band (Lissek et al, 2018), and for asymmetric transmission (Geib et al, 2021;Zhou and Baz, 2023). These active approaches have been applied to the design and realization of reconfigurable AMs.…”

Section: Active Controlmentioning

confidence: 99%

Tunable, reconfigurable, and programmable acoustic metasurfaces: A review

2023

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The advent of acoustic metasurfaces (AMs), which are the two-dimensional equivalents of metamaterials, has opened up new possibilities in wave manipulation using acoustically thin structures. Through the interaction between the acoustic waves and the subwavelength scattering, AMs exhibit versatile capabilities to control acoustic wave propagation such as by steering, focusing, and absorption. In recent years, this vibrant field has expanded to include tunable, reconfigurable, and programmable control to further expand the capacity of AMs. This paper reviews recent developments in AMs and summarizes the fundamental approaches for achieving tunable control, namely, by mechanical tuning, active control, and the use of field-responsive materials. An overview of basic concepts in each category is first presented, followed by a discussion of their applications and details about their performance. The review concludes with the outlook for future directions in this exciting field.

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“…Metamaterial design validation is typically done by fabricating small prototypes whose dynamic behavior is measured in a series of experiments in which waveguides are used to mitigate the influence of edge diffraction on the scattered fields. These methods involve placing the sample under test inside carefully designed waveguides and inverting reflection and transmission coefficients to obtain material properties such as dynamic mass density and various elastic moduli [5][6][7][8][9][10][11][12][13][14]. However, these methods are either unsuitable in applications in which the sample cannot be manipulated (e.g., in non-invasive diagnostics, imaging) or cumbersome to apply (e.g., for materials operating in aqueous environments in which the waveguide itself contaminates the scattered fields) [15].…”

Section: Introductionmentioning

confidence: 99%

Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks

Zhai

Kwon

Choi

et al. 2022

Preprint

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Conventional methods used to identify the dynamical properties of unknown media from scattered mechanical waves rely on analytical or numerical manipulations of the wave equation. These methods show their limitations in scenarios where the analyzed medium is moderately sized and the diffraction from the material edges influences the scattered fields significantly (e.g., non-destructive diagnostics, metamaterial characterization). This work shows that convolutional neural networks can interpret the diffracted fields and learn the mapping between the scattered fields and all the effective material parameters including mass density and stiffness tensors from a small set of numerical simulations. Furthermore, networks trained with synthetic data can process physical measurements and is very robust to measurement errors. More importantly, the trained network provides insight into the dynamic behavior of matter including quantitative measures of the scattered field sensitivity to each material property and how the sensitivity changes depending on the material under test.

show abstract

Tunable nonlocal purely active nonreciprocal acoustic media

Cited by 24 publications

References 25 publications

Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks

Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks

Tunable, reconfigurable, and programmable acoustic metasurfaces: A review

Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks

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