Probability-Density-Based Deep Learning Paradigm for the Fuzzy Design of Functional Metastructures
Yingtao Luo, Peng-Qi Li, Dongting Li, Yu‐Gui Peng, Zhi-Guo Geng, Shuhuan Xie, Yong Li, Andrea Alù, Jie Zhu, Xuefeng Zhu
Abstract
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.