Machine Learning

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Machine Learning (ML)

Multi-Scale and multiphysics models can be used along very different research and engineering directions. The most prominent field of multiscale materials modeling lies in predicting relations between structure, processing and properties of complex materials beyond the regimes that have been probed experimentally. Another important field of high interest in that context lies in the use of models for predicting so far unknown materials structures, properties or performance. The third essential field lies in developing adequate multiscale models that can be used for process simulation, in the best case even for online structure - property predictions of materials during manufacturing.

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  • 1. Jaimyun Jung, Jae Ik Yoon, Hyung Keun Park, Jin You Kim, Hyoung Seop Kim Bayesian approach in predicting mechanical properties of materials: Application to dual phase steels Mat. Sci. Eng. A 743 (2019) 382-290.
  • 2. Jaimyun Jung, Juwon Na, Hyung Keun Park, Jeong Min Park, Gyuwon Kim, Seungchul Lee & Hyoung Seop Kim Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis. npj Comput Mater 7 (2021) 96.
  • 3. Yongju Kim, Hyung Keun Park, Jaimyun Jung, Peyman Asghari-Rad, Seungchul Lee, Jin You Kim, Hwan Gyo Jung, Hyoung Seop Kim Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder Materials & Design 202 (2021) 109544.
  • 4. Yongju Kim, Gang Hee Gu, Peyman Asghari-Rad, Jaebum Noh, Junsuk Rho, Min Hong Seo, Hyoung Seop Kim Novel deep learning approach for practical applications of indentation Materials Today Advances 13 (2022) 100207.
  • 5. Hyung Keun Park, Jaimyun Jung, Hak Hyeon Lee, Kei Ameyama, Hyoung Seop Kim*, Efficient design of harmonic structure using a hetero-deformation induced hardening model and machine learning algorithm, Acta Materialia 244 (2023) 118583.
  • 6. Jaemin Wang, Sang Guk Jeong, Eun Seong Kim, Hyoung Seop Kim, Byeong-Joo Lee, Material-Agnostic Machine Learning Approach Enables High Relative Density in Powder Bed Fusion Products, Nature Communications 14 (2023) 6557.