Submission Deadline: 30 April 2026 View: 208 Submit to Special Issue
Professor Dr.-Ing Hui Zheng
Email: zhenghui@ustb.edu.cn
Affiliation: National Center for Materials Service Safety, University of Science and Technology Beijing, 100083, Beijing, China
Research Interests: computational methods, machine learning, elastic waves, fracture mechanics
Dr.-Ing Wenzhe Shan
Email: shan@ncu.edu.cn
Affiliation: Institute of Information Engineering, Nanchang University, 330031, Nanchang, China
Research Interests: multiscale numerical methods, PINN, machine learning, DFT applications
Recently, computational methods, such as finite element method (FEM), finite difference method (FDM), local radial basis function collocation method (LRBFCM) and so on, are extensively used to simulate and predict the behavior of physical systems. These methods enable engineers to analyze complex structures and fluid flows without the need for physical prototyping, thereby saving time and resources. Machine learning, on the other hand, introduces a data-driven approach to mechanical applications. By analyzing large datasets, machine learning algorithms can identify patterns and correlations that may not be apparent through traditional methods. The innovations of both computational methods and machine learning can provide powerful tools for simulation, analysis, and prediction of different mechanic problems.
This special issue focuses on the latest works on the mechanical problems, the numerical method, and the machine learning techniques. Analyses of mechanical problems using numerical methods or machine learning, as well as the improvement of various numerical methods or machine learning techniques, are welcome.
Potential topics include, but are not limited to the following:
- Neural Networks
- Data driven methods
- Computational methods
- Elastic waves
- Phononic crystals
- Bio mechanics
- Fracture mechanics
- Numerical modelling
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