Probability-Density-Based Deep Learning Paradigm for the Fuzzy Design of Functional Metastructures
This work addresses the fuzzy design of functional metastructures, offering a universally adaptive approach that could impact fields like acoustics, though it appears incremental as it builds on existing inverse design methods.
The paper tackles the problem of designing functional metastructures by proposing a probability-density-based deep learning paradigm that efficiently evaluates plausible structures in high-dimensional parameter space, with experiments demonstrating effectiveness and generalization in acoustics and other domains.
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 meta-structures. In contrast to other inverse design methods, our probability-density-based neural network can efficiently evaluate and accurately capture all plausible meta-structures 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 meta-structures for each targeted transmission spectrum, with experiments unequivocally demonstrating the effectiveness and generalization of the inverse design.