Submission Deadline: 31 August 2026 View: 200 Submit to Special Issue
Prof. Fajie Wang
Email: wfj88@qdu.edu.cn
Affiliation: College of Mechanical and Electrical Engineering, Qingdao University, Qingdao, 266071, China
Research Interests: computational mechanics, machine learning, boundary element method, meshless method, acoustic propagation, heat and mass transfer
Prof. Yan Gu
Email: guyan@nbu.edu.cn
Affiliation: Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, 315211, China
Research Interests: computational mechanics, machine learning, boundary element method, meshless method, fracture and damage mechanics
Prof. Bo Yu
Email: yubochina@hfut.edu.cn
Affiliation: School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China
Research Interests: computational mechanics, machine learning, boundary element method, inverse problem
With the rapid development of artificial intelligence and computational science, machine learning (ML) and data-driven modeling have emerged as transformative tools in the field of computational mechanics. Traditional numerical methods, such as the finite element, boundary element, and meshless methods, have long been the cornerstone for simulating complex physical systems. However, these conventional approaches often face challenges in computational efficiency, high-dimensional modeling, and the treatment of multi-scale or nonlinear problems. Machine learning and data-driven methods offer new possibilities to overcome these limitations by learning hidden physical relationships from data, accelerating numerical computations, and enhancing predictive capabilities under uncertainty.
This special issue aims to provide a platform for researchers and engineers to share recent advances in integrating machine learning, data-driven and novel approaches with computational mechanics. Contributions are encouraged to address theoretical developments, algorithmic innovations, and practical applications across diverse engineering domains. The issue welcomes original research articles, review papers, and case studies that explore the synergy between data science and mechanics, promoting intelligent, efficient, and interpretable computational modeling paradigms. Potential topics include, but are not limited to:
· Artificial intelligence
· Physics-informed neural networks
· Data-driven neural networks
· ML-enhanced numerical methods
· Hybrid approaches combining machine learning and traditional methods
· Surrogate and reduced-order modeling for complex systems
· Data-driven constitutive and material modeling
· AI-assisted design, optimization, and digital twins
· Operator learning
· Graph neural networks
· Novel methods in computational mechanics
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