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

Latent Map Gaussian Processes for Mixed Variable Metamodeling and Data Fusion.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Latent Map Gaussian Processes for Mixed Variable Metamodeling and Data Fusion./
Author:	Oune, Nicholas Akira.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	56 p.
Notes:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28321409
ISBN:	9798522948023

Latent Map Gaussian Processes for Mixed Variable Metamodeling and Data Fusion.
Oune, Nicholas Akira.

Latent Map Gaussian Processes for Mixed Variable Metamodeling and Data Fusion. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 56 p.

Source: Masters Abstracts International, Volume: 83-01.

Thesis (M.S.)--University of California, Irvine, 2021.

This item must not be sold to any third party vendors.

Gaussian processes (GPs) are ubiquitously used in science and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. In this paper, we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs but are also applicable to mixed data that have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a low-dimensional manifold where all qualitative inputs are represented by some quantitative features. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be used in any standard correlation function such as the Gaussian. Hence, the optimal map and the corresponding manifold can be efficiently learned by maximizing the Gaussian likelihood function. Through a wide range of analytical and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs and provide insights into how qualitative inputs affect the response or interact with each other. We also provide a neural network interpretation of LMGPs and study the effect of prior latent representations on their performance. Lastly, we demonstrate that LMGP can be applied to data assimilation problems in which data from multiple sources (e.g., simulations, mathematical models and real-world experiments) are fused to improve prediction performance.

ISBN: 9798522948023Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Computer experiments

Latent Map Gaussian Processes for Mixed Variable Metamodeling and Data Fusion.
LDR:02942nmm a2200421 4500 001 2349515
005 20230509091101.5
006 m o d
007 cr#unu||||||||
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798522948023
035 $a (MiAaPQ)AAI28321409
035 $a AAI28321409
040 $a MiAaPQ $c MiAaPQ
100 1 $a Oune, Nicholas Akira. $3 3688924
245 1 0 $a Latent Map Gaussian Processes for Mixed Variable Metamodeling and Data Fusion.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 56 p.
500 $a Source: Masters Abstracts International, Volume: 83-01.
500 $a Advisor: Bostanabad, Ramin.
502 $a Thesis (M.S.)--University of California, Irvine, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Gaussian processes (GPs) are ubiquitously used in science and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. In this paper, we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs but are also applicable to mixed data that have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a low-dimensional manifold where all qualitative inputs are represented by some quantitative features. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be used in any standard correlation function such as the Gaussian. Hence, the optimal map and the corresponding manifold can be efficiently learned by maximizing the Gaussian likelihood function. Through a wide range of analytical and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs and provide insights into how qualitative inputs affect the response or interact with each other. We also provide a neural network interpretation of LMGPs and study the effect of prior latent representations on their performance. Lastly, we demonstrate that LMGP can be applied to data assimilation problems in which data from multiple sources (e.g., simulations, mathematical models and real-world experiments) are fused to improve prediction performance.
590 $a School code: 0030.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Statistics. $3 517247
650 4 $a Artificial intelligence. $3 516317
650 4 $a Simulation. $3 644748
650 4 $a Datasets. $3 3541416
650 4 $a Estimates. $3 3561047
650 4 $a Optimization techniques. $3 3681622
650 4 $a Neural networks. $3 677449
650 4 $a Variables. $3 3548259
650 4 $a Design. $3 518875
650 4 $a Learning. $3 516521
653 $a Computer experiments
653 $a Emulation
653 $a Gaussian processes
653 $a Manifold learning
653 $a Metamodeling
653 $a Mixed-variable optimization
690 $a 0548
690 $a 0463
690 $a 0800
690 $a 0389
710 2 $a University of California, Irvine. $b Mechanical and Aerospace Engineering - M.S.. $3 2102961
773 0 $t Masters Abstracts International $g 83-01.
790 $a 0030
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28321409