A deep learning model for the topological design of 2D periodic wave barriers

Liu, Chen‐Xu; Yu, Gui‐Lan

doi:10.1111/mice.12743

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…As previously shown in music synthesis (Brunner et al., 2018), road geometry design (Wang et al., 2020), and periodic wave barriers design (Liu & Yu, 2021), the CVAE model is able to disentangle the physical features of the original high‐dimensional data by encoding them into a well‐organized latent space (Tran et al., 2017). Such disentanglement is mainly achieved through the KL divergence in Equation (), and as such, the β value in the equation controls the level of independence among the latent variables.…”

Section: Numerical Example—a Benchmark Studymentioning

confidence: 82%

Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment

Ning

Xie

2023

Computer aided Civil Eng

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This study develops an end‐to‐end deep learning framework to learn and analyze ground motions (GMs) through their latent features, and achieve reliable GM classification, selection, and generation of simulated motions. The framework is composed of an analysis workflow that transforms and reconstructs GMs through short‐time Fourier transform (STFT), encodes and decodes their latent features through convolutional variational autoencoder (CVAE), and classifies and generates GMs by grouping and interpolating latent variables. A benchmark study is established to confirm the minor difference between original GMs and the corresponding reconstructed accelerograms. The encoded latent space reveals that certain latent variables are directly linked to the dominant physical features of GMs. Resultantly, clustering latent variables using the k‐means algorithm successfully classifies GMs into different groups that vary in earthquake magnitude, soil type, field distance, and fault mechanism. By linearly interpolating two parent latent variables, simulated GMs are generated with consistent class information and matching response spectra. Furthermore, seismic fragility models are developed for a steel frame building and a concrete bridge using different sets of GMs. Using five classified, top‐ranked motions, regardless of recorded or simulated accelerograms, can achieve reasonable and efficient fragility estimates compared to the case that adopts 230 GMs. The proposed deep learning framework addresses two compelling questions regarding seismic fragility assessment: How many GMs are sufficient and what types of motions should be selected.

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Section: Numerical Example—a Benchmark Studymentioning

confidence: 82%

Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment

Ning

Xie

2023

Computer aided Civil Eng

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“…The Adam algorithm proved efficient in previous works [ 54 , 55 ] and is attracting increasing attention for optimizing NNs. The flowing equations show the flowchart for updating θ at the i th iteration:

…”

Section: Theoretical Formulations and Methodsmentioning

confidence: 99%

Inverse Design of Micro Phononic Beams Incorporating Size Effects via Tandem Neural Network

Miao

et al. 2023

Materials

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Phononic crystals of the smaller scale show a promising future in the field of vibration and sound reduction owing to their capability of accurate manipulation of elastic waves arising from size-dependent band gaps. However, manipulating band gaps is still a major challenge for existing design approaches. In order to obtain the microcomposites with desired band gaps, a data drive approach is proposed in this study. A tandem neural network is trained to establish the mapping relation between the flexural wave band gaps and the microphononic beams. The dynamic characteristics of wave motion are described using the modified coupled stress theory, and the transfer matrix method is employed to obtain the band gaps within the size effects. The results show that the proposed network enables feasible generated micro phononic beams and works better than the neural network that outputs design parameters without the help of the forward path. Moreover, even size effects are diminished with increasing unit cell length, the trained model can still generate phononic beams with anticipated band gaps. The present work can definitely pave the way to pursue new breakthroughs in micro phononic crystals and metamaterials research.

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Deep learning-based design of ternary metamaterials for isolating full-mode waves

Liu

2023

Engineering Structures

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A deep learning model for the topological design of 2D periodic wave barriers

Cited by 5 publications

References 51 publications

Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment

Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment

Inverse Design of Micro Phononic Beams Incorporating Size Effects via Tandem Neural Network

Deep learning-based design of ternary metamaterials for isolating full-mode waves

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