In quantum mechanics, a norm-squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum. This statistical property is at the core of the fuzzy structure of microcosmos. Recently, hybrid neural structures raised intense attention, resulting in various intelligent systems with far-reaching influence. Here, we propose a probability-density-based deep learning paradigm for the fuzzy design of functional metastructures. In contrast to other inverse design methods, our probability-density-based neural network can efficiently evaluate and accurately capture all plausible metastructures in a high-dimensional parameter space. Local maxima in probability density distribution correspond to the most likely candidates to meet the desired performances. We verify this universally adaptive approach in but not limited to acoustics by designing multiple metastructures for each targeted transmission spectrum, with experiments unequivocally demonstrating the effectiveness and generalization of the inverse design.
We propose a design of tunable double-band perfect absorbers based on the resonance absorption in acoustic metasurfaces with nesting helical tracks and deep-subwavelength thicknesses (<λ/30 with λ being the operation wavelength). By rotating the cover cap with an open aperture on the nesting helical tracks, we can tailor the effective lengths of resonant tubular cavities in the absorber at will, while the absorption peak frequency is flexibly shifted in spectrum and the acoustic impedance is roughly matched with air. The simulated particle velocity fields at different configurations reveal that sound absorption mainly occurs at the open aperture. Our experiment measurements agree well with the theoretical analysis and simulation, demonstrating the wide-spectrum and tunable absorption performance of the designed flat acoustic device.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.