Guest Editor(s)
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- Prof. Timon Rabczuk
Institute of Structural Mechanics, Bauhaus Universität Weimar, Weimar, Germany.
Website | E-mail
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- Assist. Prof. Jiamian Hu
Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Website | E-mail
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- Prof. Xiaoying Zhuang
Institute of Photonics, Gottfried Wilhelm Leibniz Universität Hannover, Hannover, Germany.
Website | E-mail
Special Issue Introduction
In the past decade, machine learning, including deep learning and artificial intelligence algorithms, has been increasingly applied in many fields, including related disciplines and fields of microstructure. Machine learning can aid in the performance studies of advanced engineered materials and nano/microstructures, and because of its powerful inductive capabilities, it enables the inverse design of functional materials, metamaterials, and microstructures. The emergence of these new applications also brings many challenges, such as the algorithms, optimization, efficiency, adaptability, etc. Therefore, a thorough and fundamental knowledge transfer and consolidation between the machine learning field and the micro-/nano- material, structure, and technology field is definitely required for better synergies.
This special issue will focus on new ideas and applications in designing, characterizing or preparing new materials and structures by using machine learning related technologies. We welcome manuscripts from various disciplines and particularly encourage researchers who have made significant progress in preparing microstructures with excellent mechanical, electrical or other properties through elaborate structure design. The special issue will include (not limited to) the following subjects:
● Machine learning for advanced materials engineering
● Machine learning for inverse design of metamaterials
● Machine learning for nano/microstructures
● Machine learning and data drive approach for nanomaterials characterization
● Algorithm optimization of machine learning for micro-/nano- technologies
● Physically/Mechanically informed machine learning applications
Keywords
Machine learning, functional materials, metamaterials, inverse design
Submission Deadline
30 Apr 2023
Microstructures
