Mathematical Models of Material Science: Symmetry and Applications
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1269
Special Issue Editors
Interests: multiscale analysis methods; computational material science; machine learning methods in materials science
Interests: finite element method; numerical analysis; multiscale analysis methods; computational material science
Interests: machine learning; deep neural network; optimization theory and methods
Interests: multiscale computation; deep learning method and application
Special Issue Information
Dear Colleagues,
Materials are the physical basis for human survival and development and constitute one of the three pillars of modern civilization, together with energy and information. Numerous materials have spatially symmetric configuration in the microscale, such as crystalline material, carbon nanotubes, composite material, etc. Research on material science is one of the core scientific issues in modern science and technology. In recent years, scientific computing and data-driven modeling based on mathematical models and theories have shown prominent advantages in material science research. In order to provide a solid theoretical basis and advanced algorithm for predicting and simulating the physical behaviors of materials, especially with spatially geometric symmetry, it is of great practical value and theoretical significance to study mathematical models and methods in material science. The aim of the present Special Issue is to provide an exchange platform for experts and scholars engaged in interdisciplinary research in mathematics and material science.
Dr. Hao Dong
Prof. Dr. Yufeng Nie
Dr. Xixi Jia
Dr. Yating Wang
Dr. Xiaojian Xu
Guest Editors
Manuscript Submission Information
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Keywords
- numerical simulation of partial differential equations in materials science
- multiscale methods and their mathematical theories in material science
- multiscale modeling and computation of composite materials and structures
- machine learning methods in materials science
- uncertainty quantification for random models in material science
- multiphysical modeling and computation in material science
- nonlocal models and their application in material science
- nonlinear models and their application in material science
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