Page 178 - Read Online

Page 14 of 24 He et al. Microstructures 2023;3:2023037 https://dx.doi.org/10.20517/microstructures.2023.29

Figure 3. ML for the design of wave propagation characteristics in finite meta-structures. (A) Fuzzy design of acoustic meta-structures
by combining MLP with Gaussian mixture sampling [114] . (B) Employ MLP to realize global transfer matrix prediction of active
metabeams [120] . Reproduced with the permission of Ref. [120] Copyright 2020, Elsevier. (C) Employ MLP to realize the design of
metaplates with robust edge states [122] . (D) Design of Valley Hall acoustic topological insulator by combining MLP and GA [124] .

distribution parameters of the previous layer. The probabilistic TNN model has a strong generalization
ability, greatly reducing the design cost of acoustic meta-grating wavelength division multiplexing. It
demonstrates the flexibility, diversity, and robustness of design parameters.

The acoustic cloak technology aims to reduce the sound signals generated by objects in order to reduce the
detectability of the sound detection system and achieve the effect of stealth. Ahmed et al. implemented a
design of a multilayer core-shell acoustic cloak using probabilistic TNN architecture, demonstrating its
effectiveness in solving the problem of high sensitivity of stealth cloaks to design parameters . Stealth
[118]
requirements weaken or even eliminate the disturbance of objects to the sound field, while in some practical
needs, it is desired to freely weaken or enhance the sound field in certain specific areas. In this aspect,
Zhao et al. proposed a CNN-based inverse design of metasurface phase gradient to achieve the regional
[119]
control of sound field enhancement or attenuation .

In addition, research has attempted to enable MLP to learn the physical mechanisms of a single unit and
then use it to construct a functional analysis of wave propagation in the overall structure. For example,
Wu et al. used MLP to learn the input-output relationship of longitudinal waves in non-uniform thin rod
[104]
elements and then assembled multiple MLP elements to construct a non-uniform overall structure . A
series of cascaded MLP units describe the input-output relationship of the overall structure and then use
optimization algorithms to determine the design parameters of each individual unit. Similarly, Chen et al.
used MLP for transfer matrix prediction of active metabeam elements, as shown in Figure 3B . In their
[120]
work, the metabeam unit is constructed by affixing a piezoelectric material on the main beam and
connecting a negative capacitance circuit. By using COMSOL software to obtain transfer matrices for
different capacitance values and frequencies, a dataset is constructed and used for MLP training. The global
transfer matrix of the array elements can be obtained by connecting multiple groups of MLP in sequence,
then the output and input signals of the whole metabeam can be connected.