자료유형 | E-Book |
---|---|
개인저자 | Mangal, Ankita. |
단체저자명 | Carnegie Mellon University. Materials Science and Engineering. |
서명/저자사항 | Applied Machine Learning to Predict Stress Hotspots in Materials. |
발행사항 | [S.l.] : Carnegie Mellon University., 2018 |
발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
형태사항 | 148 p. |
소장본 주기 | School code: 0041. |
ISBN | 9780438210714 |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Elizabeth A. Holm. |
요약 | This work focuses on integrating crystal plasticity based deformation models and machine learning techniques to gain data driven insights about the microstructural properties of polycrystalline metals. An inhomogeneous stress distribution in ma |
일반주제명 | Materials science. |
언어 | 영어 |
기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14999558 |