Fatigue and Fracture Behavior of Materials: Multiscale Characterization and Computational Modeling
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".
Deadline for manuscript submissions: 20 February 2026 | Viewed by 2
Special Issue Editor
Interests: analysis and design of composite materials; computational mechanics; nonlinear finite element methods; AI-assisted material analysis and design
Special Issue Information
Dear Colleagues,
This Special Issue focuses on advancements in the understanding of fatigue and fracture behavior in materials through the integration of multiscale experimental characterization and computational modeling. Given that engineering materials are increasingly being used in demanding environments, predicting their failure mechanisms has become critical for ensuring structural integrity and durability. This issue welcomes contributions that explore the fundamental and applied aspects of crack initiation and propagation, fatigue life prediction, and damage evolution across various material classes—from metals and composites to ceramics and polymers. Emphasis is placed on multiscale approaches, combining microscale observations (e.g., via SEM, X-ray CT, EBSD) with continuum and discrete modeling techniques, including finite element methods, cohesive zone models, phase-field approaches, and data-driven or machine learning-enhanced simulations. Special attention is given to the role of artificial intelligence in accelerating material behavior prediction, aiding inverse design, and improving model calibration across scales. By bridging scales and disciplines, this issue aims to foster innovation in the predictive modeling and experimental validation of material degradation phenomena, ultimately contributing to safer and more resilient engineering systems.
Dr. Ioannis Kalogeris
Guest Editor
Manuscript Submission Information
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Keywords
- fatigue
- fracture modeling
- computational mechanics
- multiscale modeling
- damage evolution
- experimental characterization
- machine learning in materials science
- data-driven modeling
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