Probability-Density-Based Deep Learning Paradigm for the Fuzzy Design of Functional Metastructures

TL;DR

This paper introduces a probability-density-based deep learning approach for the fuzzy design of functional metastructures, enabling efficient evaluation and accurate identification of plausible structures in high-dimensional spaces, with demonstrated effectiveness in acoustics.

Contribution

It presents a novel neural network paradigm that captures all plausible meta-structures via probability density, improving inverse design accuracy and efficiency over existing methods.

Findings

Successfully designed meta-structures for targeted spectra

Demonstrated generalization across different acoustic applications

Validated approach through experiments confirming effectiveness

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 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.