Special Issue "Machine Learning based Methods for Mechanics"

Submission Deadline: 31 January 2020 (closed)
Guest Editors
Prof. Xiaoying Zhuang, Leibniz Univerisity Hannover, Germany
Prof. Timon Rabczuk, Bauhaus University Weimar, Germany
Prof. Hung Nguyen Xuan, Ho Chi Minh City University of Technology (HUTECH), Vietnam


In this age of big data, machine learning techniques have been successfully applied in image processing, genomics, financial problems and even medical diagnosis. The emerging application of machine learning and big data analysis has fundamentally influenced and changed our way of how we think, plan, solve and analyze in engineering. Nevertheless, we are faced with many issues and unsolved problems when applying data drive computing and machine learning in engineering analysis.

The objective of the special issue is to invite the submissions of the works of researchers from worldwide and provide a fair overview on the state of the art on theories, methods and techniques contributed to the machine learning for mechanics and applications, and also the remaining issues and limitations, thus promoting further research interests.

Possible topics (inviting more ideas): 
Papers on topics related to new theory, methods and applications related to the machine learning for mechanics not only to theory but also to experiments are encouraged to submit to the special issue. 

We outline the following possible related topics but are not restricted to: 
• Machine learning based solutions of PDEs
• Data driven constitutive modelling
• Visualization and visual analytics of mechanical engineering
• Big data for design and optimization 
• Machine learning assisted high performance computing
• Data-driven computing in dynamics and molecular dynamics
• Machine learning for uncertainties analysis and stochastic model
• Data-driven simulation techniques
• Machine learning for discrete particle-based method

Published Papers
  • A Reinforcement Learning System for Fault Detection and Diagnosis in Mechatronic Systems
  • Abstract With the increasing demand for the automation of operations and processes in mechatronic systems, fault detection and diagnosis has become a major topic to guarantee the process performance. There exist numerous studies on the topic of applying artificial intelligence methods for fault detection and diagnosis. However, much of the focus has been given on the detection of faults. In terms of the diagnosis of faults, on one hand, assumptions are required, which restricts the diagnosis range. On the other hand, different faults with similar symptoms cannot be distinguished, especially when the model is not trained by plenty of data. In… More
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  • Investigation of Granite Deformation Process under Axial Load Using LSTM-Based Architectures
  • Abstract Granite is generally composed of quartz, biotite, feldspar, and cracks. The changes in digital parameters of these compositions reflect the detail of the deformation process of the rock. Therefore, the estimation of the changes in digital parameters of the compositions is much helpful to understand the deformation and failure stages of the rock. In the current study, after dividing the frames in the video images photographed during the axial compression test into four parts (or, the upper left, upper right, lower left, and lower right ones), the digital parameters of various compositions in each part were then extracted. Using these… More
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  • Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks
  • Abstract The crack is a common pavement failure problem. A lack of periodic maintenance will result in extending the cracks and damage the pavement, which will affect the normal use of the road. Therefore, it is significant to establish an efficient intelligent identification model for pavement cracks. The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix. It has been widely used in geotechnical engineering, computer vision, medicine, and other fields. However, there are three major problems in the application of neural networks to crack identification. There are too… More
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