Willis materials are complex media characterized by four macroscopic material parameters, the conventional mass density and bulk modulus and two additional Willis coupling terms, which have been shown to enable unsurpassed control over the propagation of mechanical waves. However, virtually all previous studies on Willis materials involved passive structures which have been shown to have limitations in terms of achievable Willis coupling terms. In this article, we show experimentally that linear active Willis metamaterials breaking these constraints enable highly non-reciprocal sound transport in very subwavelength structures, a feature unachievable through other methods. Furthermore, we present an experimental procedure to extract the effective material parameters expressed in terms of acoustic polarizabilities for media in which the Willis coupling terms are allowed to vary independently. The approach presented here will enable a new generation of Willis materials for enhanced sound control and improved acoustic imaging and signal processing.

Noise is a long standing societal problem that has recently been linked to serious health consequences. Despite decades of research on noise mitigation techniques, existing methods have significant limitations including inability to silence broadband noise and shield large volumes. Here we show theoretically and experimentally that acoustic bianisotropic materials with non-zero strain to momentum coupling are remarkably effective sound barriers. They surpass state-of-the-art sound isolators in terms of attenuation, bandwidth, and shielded volume. We implement our barriers with very compact active meta-atoms that owe their small size to their local response to external sound. Moreover, our active approach is not constrained by feedback stabilization requirements, in stark contrast with all traditional active sound control systems. Consequently, bianisotropic sound barriers have the potential to revolutionize noise control technologies and provide much needed solutions to an increasingly important and difficult challenge.

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.

No abstract

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.