Innovative Topology Optimization Methods and Sustainable Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 10 October 2025 | Viewed by 648
Special Issue Editor
Special Issue Information
Dear Colleagues,
Topology optimization has emerged as a transformative approach in structural and mechanical design, enabling the development of lightweight, high-performance structures optimized for various constraints and applications. This computational method has significantly advanced in recent years, incorporating multi-physics simulations, reliability-based design considerations, and sustainability aspects. The integration of topology optimization with cutting-edge numerical techniques, such as finite element analysis (FEA) and artificial intelligence (AI)-driven methods, has broadened its applicability across engineering disciplines.
This Special Issue aims to gather novel contributions on the latest advancements in topology optimization methods and their applications in structural engineering, aerospace, biomechanics, and beyond. Topics of interest include, but are not limited to, reliability-based topology optimization, manufacturing constraints, multi-material optimization, and sustainability-driven design strategies. We invite researchers and practitioners to submit original research articles, case studies, and review papers to contribute to this growing field.
If you are unable to make a contribution at this time, we encourage you to share this invitation with colleagues who may be interested. For any inquiries or requests for extensions, please feel free to contact us.
Thank you for your time and consideration. We look forward to your contributions.
Dr. Muayad Habashneh
Guest Editor
Manuscript Submission Information
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Keywords
- topology optimization
- reliability-based design
- computational mechanics
- finite element analysis (FEA)
- sustainability in structural design
- multi-material optimization
- additive manufacturing and design
- uncertainty in optimization
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