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020 ▼a 9780438173064
035 ▼a (MiAaPQ)AAI10838565
035 ▼a (MiAaPQ)columbia:14811
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 530
1001 ▼a Tal, David.
24510 ▼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.
5021 ▼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
71020 ▼a Columbia University. ▼b Civil Engineering and Engineering Mechanics.
7730 ▼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
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999640 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자