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Abstract Active metamaterials address fundamental limitations of passive media and have widely been recognized as necessary in numerous compelling applications such as cloaking and extreme noise absorption. However, most practical devices of interest have yet to be realized due to the lack of a suitable strategy for implementing bulk active metamaterials—those that involve interacting cells and functionality beyond one dimension. Here, we present such an active acoustic metamaterial design with bulk modulus and anisotropic mass density that can be independently programmed over wide value ranges. We demonstrate this ability experimentally in several examples, targeting acoustic properties that are hard to access otherwise, such as a bulk modulus significantly smaller than air, strong mass density anisotropy, and complex bulk modulus and mass density for high reflectionless sound absorption. This work enables the transition of active acoustic metamaterials from isolated proof-of-concept demonstrations to versatile bulk materials. 

Identifying the material properties of unknown media is an important scientific/engineering challenge in areas as varied as in-vivo tissue health diagnostics and metamaterial characterization. Currently, techniques exist to retrieve the material parameters of large unknown media from elastic wave scattering in free-space using analytical or numerical methods. However, applying these methods to small samples on the order of few wavelengths in diameter is challenging, as the fields scattered by these samples become significantly contaminated by diffraction from the sample edges. Here, we propose a method to extract the material parameters of small samples using convolutional neural networks trained to learn the mapping between far-field echoes and the material parameters. Networks were trained with synthetic time domain echo data obtained by simulating the free-space scattering of sound from unknown media underwater. Results show that neural networks can accurately predict effective material parameters such as mass density, bulk modulus, and shear modulus even when small training sets are used. Furthermore, we demonstrate in experiments executed in a water tank that the networks trained with synthetic data can accurately estimate the material properties of fabricated metamaterial samples from single-point echo measurements performed in the far-field. This work highlights the effectiveness of our approach to identify unknown media using far-field acoustic reflection dominated by diffraction fields and will open a new avenue toward acoustic sensing techniques. 

Active acoustic metamaterials are one path to acoustic properties difficult to realize with passive structures, especially for broadband applications. Here, we experimentally demonstrate a 2D metamaterial composed of coupled sensor-driver unit cells with effective bulk modulus ([Formula: see text]) precisely tunable through adjustments of the amplitude and phase of the transfer function between pairs of sensors and drivers present in each cell. This work adopts the concepts of our previous theoretical study on polarized sources to realize acoustic metamaterials in which the active unit cells are strongly interacting with each other. To demonstrate the capability of our active metamaterial to produce on-demand negative, fractional, and large [Formula: see text], we matched the scattered field from an incident pulse measured in a 2D waveguide with the sound scattered by equivalent continuous materials obtained in numerical simulations. Our approach benefits from being highly scalable, as the unit cells are independently controlled and any number of them can be arranged to form arbitrary geometries without added computational complexity. 

Abstract 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.