| 研究実績の概要 |
In this year, I have three main works, including a review article in terms of deep learning in mechanical metamaterials, a research article in terms of multiphase metamaterials with highly variable stiffness, and a research article in terms of text-to-microstructure generation using deep learning.
In the review article, I provide a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, I highlight the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence.
In the second article, I propose three multiphase metamaterials derived from triply periodic minimal surfaces. The multiphase metamaterials possess highly variable stiffness based on thermally-induced phase transition.
In the third article, I propose a new deep learning framework that can generate different and diverse material microstructures using text prompts.
I have published 6 peer-reviewed papers on international journals and 1 patent during this academic year.
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