東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Multiscale Modeling of Polyurethane Properties Via Latent Variables with Hierarchical Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multiscale Modeling of Polyurethane Properties Via Latent Variables with Hierarchical Machine Learning./
Author:	Pugar, Joseph Andrew.
Description:	1 online resource (206 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
Contained By:	Dissertations Abstracts International84-08B.
Subject:	Polymer chemistry. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30248117click for full text (PQDT)
ISBN:	9798371980069

Multiscale Modeling of Polyurethane Properties Via Latent Variables with Hierarchical Machine Learning.
Pugar, Joseph Andrew.

Multiscale Modeling of Polyurethane Properties Via Latent Variables with Hierarchical Machine Learning. - 1 online resource (206 pages)

Source: Dissertations Abstracts International, Volume: 84-08, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2023.

Includes bibliographical references

The objective of the following dissertation was to develop Hierarchical Machine Learning models to accurately predict bulk properties of polyurethanes with domain knowledge from small datasets. Polyurethanes find application in coatings, foams, and solid elastomers but the range of processing conditions and the diversity of monomers results in limited datasets in practice and as a result statistical modeling has not been pursued extensively in place of high-throughput experiments, bias brute force modeling, or assumptive analytical treatments. By introducing experimental and computational data describing the multiscale forces in polyurethanes with Hierarchical Machine Learning, interpretable and high predictive-strength models were studied. Four major property types were the subject of modeling-thermal, rheological, mechanical, and failure. Each has unique hypotheses as to what underlying physical trend accurately predicts the property. Testing was performed on withheld sets of training data and secondary validation sets of unobserved chemical formulations were used to further analyze the generalizability of prediction. Through model performance and feature selection/analysis, material and property design principles could be extracted from model outputs.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798371980069Subjects--Topical Terms:

3173488
Polymer chemistry.
Subjects--Index Terms:

Latent variablesIndex Terms--Genre/Form:

542853
Electronic books.

Multiscale Modeling of Polyurethane Properties Via Latent Variables with Hierarchical Machine Learning.
LDR:02613nmm a2200373K 4500 001 2360717
005 20231015184503.5
006 m o d
007 cr mn ---uuuuu
008 241011s2023 xx obm 000 0 eng d
020 $a 9798371980069
035 $a (MiAaPQ)AAI30248117
035 $a AAI30248117
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Pugar, Joseph Andrew. $3 3701348
245 1 0 $a Multiscale Modeling of Polyurethane Properties Via Latent Variables with Hierarchical Machine Learning.
264 0 $c 2023
300 $a 1 online resource (206 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
500 $a Advisor: Washburn, Newell R.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2023.
504 $a Includes bibliographical references
520 $a The objective of the following dissertation was to develop Hierarchical Machine Learning models to accurately predict bulk properties of polyurethanes with domain knowledge from small datasets. Polyurethanes find application in coatings, foams, and solid elastomers but the range of processing conditions and the diversity of monomers results in limited datasets in practice and as a result statistical modeling has not been pursued extensively in place of high-throughput experiments, bias brute force modeling, or assumptive analytical treatments. By introducing experimental and computational data describing the multiscale forces in polyurethanes with Hierarchical Machine Learning, interpretable and high predictive-strength models were studied. Four major property types were the subject of modeling-thermal, rheological, mechanical, and failure. Each has unique hypotheses as to what underlying physical trend accurately predicts the property. Testing was performed on withheld sets of training data and secondary validation sets of unobserved chemical formulations were used to further analyze the generalizability of prediction. Through model performance and feature selection/analysis, material and property design principles could be extracted from model outputs.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Polymer chemistry. $3 3173488
650 4 $a Materials science. $3 543314
650 4 $a Chemical engineering. $3 560457
653 $a Latent variables
653 $a Machine learning
653 $a Polyurethane
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0794
690 $a 0495
690 $a 0542
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Carnegie Mellon University. $b Materials Science and Engineering. $3 3281156
773 0 $t Dissertations Abstracts International $g 84-08B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30248117 $z click for full text (PQDT)