Submission Deadline: 01 July 2026 View: 280 Submit to Special Issue
Prof. Zhuojia Fu
Email: paul212063@hhu.edu.cn
Affiliation: College of Mechanics and Engineering Science, Hohai University, Changzhou, 213200, China
Research Interests: computational mechanical methods, artificial intelligence technologies, numerical modelling, boundary element methods, meshless methods
Prof. Linxin Peng
Email: penglx@gxu.edu.cn
Affiliation: School of Civil Engineering and Architecture, Guangxi University, Nanning, China
Research Interests: computational solid mechanics; deep learning and composite mechanics; meshless methods; plate and shell structures
Assoc. Prof. Hanshu Chen
Email: chenhanshu@hhu.edu.cn
Affiliation: College of Mechanics and Engineering Science, Hohai University, Changzhou, 213200, China
Homepage: https://www.researchgate.net/profile/Hanshu-Chen-3
Research Interests: uncertainty quantification, computational mechanical methods, neural Networks model, nonlinear dynamics
Assoc. Prof. Changting Zhong
Email: zhongct@hainanu.edu.cn
Affiliation: School of Civil Engineering and Architecture, Hainan University, Haikou, China
Research Interests: optimization, metaheuristic algorithms, structural reliability, artificial intelligence, machine learning, deep learning
Dr. Hui Huo
Email: huohui_hh@163.com
Affiliation: Department of Engineering Mechanics, Xi'an University of Technology, Xi'an, 710021, China
Research Interests: uncertainty quantification, reliability assessment, stochastic finite element method
Recently, artificial intelligence (AI) technologies, notably machine learning and other data-driven methods, have made remarkable progress. Their ability to extract information and process data at high speeds has facilitated their integration into numerous academic fields. However, traditional computational mechanics continues to face challenges, including incomplete physical modelling, low computational efficiency, and insufficient accuracy, particularly in scenarios involving complex environments, complicated models, and large deformations. Furthermore, conventional optimization methods are significantly limited when tackling complex problems involving unclear expression of objective functions, multiple objectives, as well as discontinuous, non-differentiable, or highly nonlinear constraints. Therefore, integrating data-driven AI with knowledge-driven traditional computational mechanics and optimization design has become a significant focus of research. This integration is instrumental in developing novel intelligent methodologies for computational mechanics and structural optimization, thereby broadening the scope of theoretical and methodological solutions for complex engineering problems and revealing substantial application potential.
This special issue focuses on the latest research in computational mechanics, AI techniques, and optimization methods. We welcome submissions that analyse computational mechanics and optimization problems using numerical methods or AI techniques, as well as research focused on improving these methods and techniques.
Potential topics include, but are not limited to the following:
- Neural Networks
- Data-driven methods
- Artificial intelligence (AI) technologies
- Advanced computational mechanical methods
- Uncertainty quantification
- Structural reliability assessment
- Structural optimization
- Numerical modelling
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