Guest Editors
Prof. Xu Long
Email: xulong@nwpu.edu.cn
Affiliation: School of Mechanics and Transportation Engineering, Northwestern Polytechnical University, Xi'an, China
Homepage:
Research Interests: protective material and structures, multi-field multi-scale material constitutive and damage model, electronic packaging mechanics
Prof. Yanwei Dai
Email: ywdai@bjut.edu.cn
Affiliation: Institute of Electronics Packaging Technology and Reliability, Beijing University of Technology, Beijing, China
Homepage:
Research Interests: solid mechanics, reliability of integrated circuit packaging technologies
Prof. Jianwei Zhang
Email: zhangjianwei@zzu.edu.cn
Affiliation: School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou, China
Homepage:
Research Interests: anti-fatigue manufacturing and mechanical properties of polymer materials
Prof. Fahmi Zaïri
Email: fahmi.zairi@polytech-lille.fr
Affiliation: Civil Engineering and Geo-Environmental Laboratory (LGCgE), University of Lille, Lille, France
Homepage:
Research Interests: mechanics of materials
Summary
The development of modern functional and multifunctional materials is increasingly driven by the materials genome paradigm and integrated computational materials engineering, where data, physics-based modeling, and advanced computation are tightly coupled to accelerate materials innovation. Advanced materials and structures, ranging from multiphase composites to microstructured and multifunctional systems, exhibit complex multiscale architectures and multiphysical couplings. Their structural performance, functional response, and long-term reliability are strongly influenced by processing history, microstructural heterogeneity, and service environments, creating urgent needs for predictive, design-oriented modeling frameworks.
Recent progress in physics-informed AI, high-performance computing, and big-data analytics offers transformative opportunities to bridge materials genome concepts with engineering-scale design and manufacturing. By embedding physical laws and multiscale mechanisms into machine learning and data-driven models, physics-informed AI enables efficient discovery of structure–process–property–performance relationships, rapid surrogate modeling, and reliability-aware optimization. When combined with multiscale simulations and experimental or manufacturing data, these approaches support the integrated design, performance prediction, and lifecycle reliability assessment of advanced materials and structures.
This Special Issue aims to highlight cutting-edge research at the intersection of physics-informed AI, multiscale modeling, and data-enabled materials engineering, with a focus on design, manufacturing relevance, and reliability of advanced material and structure systems. Suggested themes include, but are not limited to:
• Materials genome and engineering frameworks for advanced materials
• Modeling of damage, degradation, and reliability in heterogeneous materials
• Multiscale and multiphysics modeling of functional and multifunctional advanced structures
• Physics-informed AI for structure–process–property–performance relationships
• Data-driven and hybrid constitutive modeling across geometric scales
• Surrogate modeling and high-performance computing in materials design
• AI-assisted optimization for heterogeneous material, structures and architected systems
• Integration of experiments, simulations, and manufacturing data
• Data analytics and machine learning for advanced material manufacturing
Keywords
advanced materials and structures, multiphysics coupling, multiscale modeling, heterogeneity, data-driven materials science, mechanical reliability, physics-informed AI, surrogate modeling, materials design and manufacturing
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