Seminar & Colloquium
[세미나: 5월 15일(월), 오후 1시 30분] Prof. Marat Latypov, University of Arizona
Title
Machine learning of effective properties of heterogenous materials with limited microstructure information
Speaker
Prof. Marat Latypov, University of Arizona
Education
- PhD: Pohang University of Science and Technology, Pohang, South Korea, 2014
- Engineering Diploma: Engineering Physics, Ufa State Aviation Technical University, Ufa, Russia, 2011
Professional Experiences
- August 2021 ~ : Assistant Professor, University of Arizona, Tucson, AZ
- 2019 ~ 2021: Senior Scientist, Novelis Global Research & Technology Center, Novelis Inc, Kennesaw, GA
- 2017 ~ 2019: Postdoctoral Scholar, University of California, Santa Barbara, CA
- 2015 ~ 2017: Postdoctoral Researcher, Georgia Tech/CNRS Laboratory at Georgia Tech Lorraine, Metz, France
| Date | Monday, May 15th, 2023
| Time | 13:30 ~
| Venue | 33동 226호
온라인 강의 (https://snu-ac-kr.zoom.us/j/91625864888?pwd=UFc2WXJKQUxMVFkxalBGd05LTWswUT09)
회의 ID : 916 2586 4888
암호 : 018627
[Abstract]
Microstructure--property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite elements) based on microstructure representative volumes. The need in high-resolution 3D microstructure data limits a wider adoption of microstructure-sensitive 3D models. In this work, we present machine learning (ML) strategies that are less demanding in terms of 3D microstructure input. We will first discuss ML approaches to modeling effective properties of two-phase materials directly from 2D microstructure sections. We then present graph-based ML models for mechanical properties of polycrystalline materials based on grain centroid representations of polycrystals.
| Host | 이명규 교수 (02-880-1711)