| LDR | | 01569nam u200373 4500 |
| 001 | | 000000419299 |
| 005 | | 20190215163551 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438210714 |
| 035 | |
▼a (MiAaPQ)AAI10837451 |
| 035 | |
▼a (MiAaPQ)cmu:10270 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 620.11 |
| 100 | 1 |
▼a Mangal, Ankita. |
| 245 | 10 |
▼a Applied Machine Learning to Predict Stress Hotspots in Materials. |
| 260 | |
▼a [S.l.]:
▼b Carnegie Mellon University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 148 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Elizabeth A. Holm. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Carnegie Mellon University, 2018. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0041. |
| 650 | 4 |
▼a Materials science. |
| 690 | |
▼a 0794 |
| 710 | 20 |
▼a Carnegie Mellon University.
▼b Materials Science and Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0041 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999558
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |