Submission Deadline: 30 April 2025 View: 259 Submit to Special Issue
Multiscale modeling and simulation have emerged as powerful tools for studying and discovering advanced materials, including bio-inspired materials, composite materials, and metamaterials. These methods, which encompass bottom-up simulation and top-down design approaches, play a crucial role in materials science by enabling researchers to explore phenomena across a range of spatial and temporal scales.
Two primary frameworks—concurrent and hierarchical—have been developed in the realm of multiscale methods. While these frameworks provide valuable insights into material behavior, challenges persist in efficiently transferring information between different scales. Recent advancements in machine learning present a promising alternative solution to address these challenges.
This special issue aims to showcase the latest research in the field of multiscale modeling and simulation, with a particular emphasis on computational approaches spanning various spatial and temporal scales. By facilitating interdisciplinary collaboration among experts in physics, chemistry, biology, and engineering, the issue seeks to advance our understanding of materials science and engineering. It aligns with current trends in materials research and the scope of CMES.
This special issue seeks to advance the frontier of multiscale modeling and simulation in materials science and engineering by providing a platform for researchers to exchange innovative ideas and achievements. The special issue invites contributions on a wide range of topics, including but not limited to:
1. Comprehensive reviews of stat-of-the-art multiscale methods.
2. Development and application of novel multiscale methods for simulation.
3. Investigations into advanced materials using multiscale approaches, with a focus on bio-inspired materials, composite materials, and metamaterials.
4. Utilization of machine learning and deep learning techniques in multiscale modeling to enhance material discovery and design processes.
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