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Applied Machine Learning to Predict Stress Hotspots in Materials
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Applied Machine Learning to Predict Stress Hotspots in Materials. |
| 개인저자 | Mangal, Ankita. |
| 단체저자명 | Carnegie Mellon University. Materials Science and Engineering. |
| 발행사항 | [S.l.]: Carnegie Mellon University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 148 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438210714 |
| 학위논문주기 | Thesis (Ph.D.)--Carnegie Mellon University, 2018. |
| 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 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
