Guest Editors
Prof. Sy Dzung Nguyen, Ton Duc Thang University, Vietnam
Prof. Seung-Bok Choi, Inha University, South Korea
Prof. Quoc Hung Nguyen, Vietnamese-German University, Vietnam
Prof. Trung Nguyen-Thoi, Ton Duc Thang University, Vietnam
Summary
Deep learning (DL) is a subset of machine learning. Independent of hand-crafted features like local patterns, a histogram of gradients, etc., DL performs its own patterns establishing and hierarchical information extraction to learn global features layer-wise from data. Beginning from initial layers to learn low-level features, it then moves up the hierarchy to learn a more abstract representation of the data, step by step. This special issue focuses on exploiting thus the capability of DL to build mathematical models depicting the dynamic response of technique systems more objectively and accurately, as well as on developing applications deriving from these mathematical models. The submission should describe original research works in topics and technical areas of interest including but not limited as follows:
1) Data-driven based Intelligent Structures (DIS);
2) Applications of DIS for surveying/applying Smart Materials, Data Science, identification, prediction, measurement, control of technique systems.
Keywords
Machine learning, Deep learning, AI-based Data Science, Deep Learninng for Inverse Problems, Intelligence Control.
Published Papers
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Open Access
ARTICLE
A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model
Yiping Sun, Jiadui Chen, Qiang Bai, Xuefeng Zhao, Meng Tao
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1693-1716, 2022, DOI:10.32604/cmes.2022.019336
（This article belongs to this Special Issue:
Data-Driven Model and Deep Learning for Advanced Smart Materials and Structures)
Abstract Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest. Traditional forward research methods are unable to manipulate sound waves accurately and effectively, are difficult to analyse, have time-consuming solution processes, and have large optimization search spaces. To address these issues, this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance. First, an autoencoder (AE) model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating,…
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Open Access
ARTICLE
Predicting the Reflection Coefficient of a Viscoelastic Coating Containing a Cylindrical Cavity Based on an Artificial Neural Network Model
Yiping Sun, Qiang Bai, Xuefeng Zhao, Meng Tao
CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1149-1170, 2022, DOI:10.32604/cmes.2022.017760
（This article belongs to this Special Issue:
Data-Driven Model and Deep Learning for Advanced Smart Materials and Structures)
Abstract A cavity viscoelastic structure has a good sound absorption performance and is often used as a reflective baffle or sound absorption cover in underwater acoustic structures. The acoustic performance field has become a key research direction worldwide. Because of the time-consuming shortcomings of the traditional numerical analysis method and the high cost of the experimental method for measuring the reflection coefficient to evaluate the acoustic performance of coatings, this innovative study predicted the reflection coefficient of a viscoelastic coating containing a cylindrical cavity based on an artificial neural network (ANN). First, the mapping relationship between the input characteristics and reflection…
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