LDR | | 00000nmm u2200205 4500 |
001 | | 000000331160 |
005 | | 20241112173739 |
008 | | 181129s2018 ||| | | | eng d |
020 | |
▼a 9780438173064 |
035 | |
▼a (MiAaPQ)AAI10838565 |
035 | |
▼a (MiAaPQ)columbia:14811 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
049 | 1 |
▼f DP |
082 | 0 |
▼a 530 |
100 | 1 |
▼a Tal, David. |
245 | 10 |
▼a Uncertainty Quantification in CompositeMaterials. |
260 | |
▼a [S.l.] :
▼b Columbia University.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 112 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
500 | |
▼a Adviser: Jacob Fish. |
502 | 1 |
▼a Thesis (Ph.D.)--Columbia University, 2018. |
520 | |
▼a The random nature of the micro-structural attributes in materials in general and composite material systems in particular requires expansion of material modeling in a way that will incorporate their inherent uncertainty and predict its impact on |
520 | |
▼a The work presented in this essay takes a few steps towards an improved material modeling approach which encompasses structural randomness in order to produce a more realistic representation of material systems. For this end a computational frame |
520 | |
▼a Image processing and analysis in one of the material systems extended the original scope of this work to solving a machine vision and learning problem. Object segmentation for the purpose object and pattern recognition has been a long standing s |
590 | |
▼a School code: 0054. |
650 | 4 |
▼a Computational physics. |
650 | 4 |
▼a Statistics. |
690 | |
▼a 0216 |
690 | |
▼a 0463 |
710 | 20 |
▼a Columbia University.
▼b Civil Engineering and Engineering Mechanics. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0054 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999640
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자
▼b 관리자 |