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.

Non-Hermitian wave system has attracted intense attentions in the past decade since it reveals interesting physics and generates various counterintuitive effects. However, in the diffusive system that is inherently non-Hermitian with natural dissipation, the robust control of heat flow is hitherto still a challenge. Here we introduce the skin effect into diffusive systems. Different from the skin effect in wave systems, where asymmetric couplings were enabled by dynamic modulations or judicious gain/loss engineering, asymmetric couplings of the temperature fields in diffusive systems can be realized by directly contacted metamaterial channels. Topological heat funneling is further presented, where the temperature field automatically concentrates towards a designated position and shows a strong immunity against the defects. Our work indicates that the diffusive system can provide a distinctive platform for exploring non-Hermitian physics as well as thermal topology.

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Valley states, labeling the frequency extrema in momentum space, carry a new degree of freedom (valley pseudospin) for topological transport of sound in sonic crystals. Recently, the field of valley acoustics has become a hotspot due to its potentials in developing various topological-insulator-based devices. In most previous works, topological valley transport is implemented at the interfaces of two connected artificial crystals. With respect to the interface, the mirror symmetry of crystal structures supports either even-mode or odd-mode valley states. In this work, we propose a physical insight of transforming one hexagonal crystal into a virtual lattice by utilizing the mirror operation of rigid or soft boundaries, which greatly reduces the dimension of the acoustic structure and provides a possible way to implement the programmable routing of topological propagation. We investigate two cases that the rigid and soft boundaries are introduced either at the edge or inside a single hexagonal crystal. Our results clearly demonstrate the high-transmission valley transport along the folded boundaries, where reflection or scattering is prohibited at the sharp bending or corners due to topological protection. Three functional devices are exemplified, which are single-crystal-based topological delay-line filter, delay-line switcher and beam splitter. Our work reveals the inherent relation between the field symmetries of valley states and structural symmetries of sonic crystals. Programmable routing of topological sound transport through boundary engineering provides a platform for developing integrated and versatile topological-insulator-based devices.