Guest Editors
Dr. Aman Garg
Email: aman_garg@hust.edu.cn
Affiliation: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 430074, Wuhan, China
Homepage:
Research Interests: machine learning, bio-inspired composites, advanced construction materials, CNT reinforced composites, impact-induced failure
Dr. Akhil Garg
Email: Akhil.Garg@xjtlu.edu.cn
Affiliation: Department of Chemistry and Materials Science, Xi’an Jiaotong-Liverpool University, 215123, Xi’an, China
Homepage:
Research Interests: application of AI methods for battery recycling, machine learning for battery synthesis, genetic programming, AI-driven topology optimization for cleaner energy
Dr. H.D. Chalak
Email: chalakhd@nitkkr.ac.in
Affiliation: Department of Civil Engineering, National Institute of Technology Kurukshetra, 136119, Kurukshetra, India
Homepage:
Research Interests: machine learning, cementous materials, high-rise buildings, beam-column connection, functionally graded material, application of FRP in civil engineering, ECC
Dr. Roshan Raman
Email: Roshanraman@ncuindia.edu
Affiliation: Department of Multidisciplinary Engineering, The NorthCap University, 122017, Gurugram, India
Homepage:
Research Interests: thermal engineering, deep Learning, IC engines, alternative fuels, HMT, RAC and renewable energy
Summary
Recent advancements in machine learning (ML) are revolutionizing the way materials and structural mechanics are studied and engineered. Traditional numerical and analytical models, while robust, often require high computational cost and rely on idealized assumptions. In contrast, ML-based approaches enable rapid predictions, surrogate modeling, uncertainty quantification, inverse analysis, and design optimization — even for complex and nonlinear behaviors in materials and structures.
This Special Issue aims to bring together cutting-edge research that leverages machine learning techniques in the mechanics of materials and structures. It invites contributions that integrate ML with computational mechanics, finite element methods, material characterization, structural health monitoring, damage detection, multiscale modeling, and smart materials.
We particularly welcome interdisciplinary approaches that bridge artificial intelligence, data-driven modeling, and physics-based simulation in the context of functional and multifunctional materials. This aligns with the journal's scope, supporting advancements in materials genome, integrated materials science, and smart data analysis for next-generation structures.
Suggested Themes
· ML-assisted prediction of mechanical behavior in advanced materials
· Surrogate models for fracture, fatigue, and failure analysis
· Physics-informed neural networks (PINNs) in computational mechanics
· Transfer learning and domain adaptation in materials engineering
· Inverse design of composite, bio-inspired, and architected materials
· ML in finite element modeling and adaptive mesh refinement
· Explainable AI (XAI) for materials characterization
· Multiscale and multi-fidelity ML approaches
· Smart sensors and structural health monitoring using ML
· Data-driven discovery of constitutive models
Keywords
Machine learning, Structural mechanics, Materials modeling, Surrogate modeling, Physics-informed learning, Finite element method, Data-driven mechanics, Structural health monitoring, Deep learning, Inverse design, Energy saving materials, Next-Gen materials for EVs
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