Page 172 - Read Online

Page 8 of 24 He et al. Microstructures 2023;3:2023037 https://dx.doi.org/10.20517/microstructures.2023.29

Figure 1. ML in solving the problem of properties prediction and inverse design for meta-structures.

layered phononic crystals and compared their efficiency and accuracy. The input parameters of the neural
networks are one to three selected from the filling fraction, mass density ratio, and shear-designed structure.
The accuracy in predicting band structures is achieved using a single parameter (fill fraction), while in the
case of multiple parameters, MLP outperforms RBF-NN.

Another type of work is to use trained forward models to assist in the selection of alternative structures after
inverse design. Zhang et al. constructed a digital structural genome using CNN to achieve structural
screening with specified elastic wave properties . For representative volume elements (RVEs) of size 5 × 5,
[93]
25
each unit has two coding forms, with a total of 2 possible configurations, which makes it difficult to find
configurations with target elastic wave properties. Their approach is to calculate the band structures of a
small portion of RVEs using a finite element method and extract wave properties to construct a dataset.
Then, by using data-driven CNN to predict the elastic wave properties of all possible configurations, a
digitally structured genome is constructed. For a set of target elastic wave properties, the corresponding
structure can be found in the genome. Jiang et al. proposed a novel way to inverse design similar digitally
coded metamaterials, as shown in Figure 2A . This work can be divided into three steps: first, train CNN
[94]
to predict the band structures; second, train GAN to generate digitally coded metamaterials from band
structures; and finally, take out the generator of GAN and connect it with CNN. The overall workflow is as
follows: the generator takes random noise and target band structures as inputs, generating a series of
alternative structures. Predict the corresponding band structures of all candidate structures through CNN
and then compare them with the target band structure to screen the best structure. Almost at the same time,
Han et al. employed the same design process to realize inverse design of digitally coded metamaterials with