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Deep Learning in Engineering Mechani...

Finol Berrueta, David.

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Deep Learning in Engineering Mechanics: Wave Propagation and Dynamics Implementations.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning in Engineering Mechanics: Wave Propagation and Dynamics Implementations./
Author:	Finol Berrueta, David.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	79 p.
Notes:	Source: Masters Abstracts International, Volume: 81-02.
Contained By:	Masters Abstracts International81-02.
Subject:	Aerospace engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10981734
ISBN:	9781085562751

Deep Learning in Engineering Mechanics: Wave Propagation and Dynamics Implementations.
Finol Berrueta, David.

Deep Learning in Engineering Mechanics: Wave Propagation and Dynamics Implementations. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 79 p.

Source: Masters Abstracts International, Volume: 81-02.

Thesis (M.S.)--Illinois Institute of Technology, 2019.

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

With the advent of Artiﬁcial Intelligence research in the 1960s, the need for intelligent systems that are able to truly comprehend the physical world around them became relevant. Signiﬁcant milestones in the realm of machine learning and, in particular, deep learning during the past decade have led to advanced data-driven models that are able to approximate complex functions from pure observations. When it comes to the application of physics-based scenarios, the vast majority of these models rely on statistical and optimization constructs, leaving minimal room in their development for the physics-driven frameworks that more traditional engineering and science ﬁelds have been developing for centuries. On the other hand, the more traditional engineering ﬁelds, such as mechanics, have evolved on a diﬀerent set of modeling tools that are mostly based on physics driven assumptions and equations, typically aided by statistical tools for uncertainty handling. Deep learning models can provide signiﬁcant implementation advantages in commercial systems over traditional engineering modeling tools in the current economies of scale, but they tend to lack the strong reliability their counterparts naturally allow. The work presented in this thesis is aimed at assessing the potential of deep learning tools, such as Convolutional Neural Networks and Long Short-Term Memory Networks, as data-driven models in engineering mechanics, with a major focus on vibration problems. In particular, two implementation cases are presented: a data driven surrogate model to a Phononic eigenvalue problem, and a physics-learning model in rigid-body dynamics scenario. Through the applications presented, this work that shows select deep learning architectures can appropriately approximate complex functions found in engineering mechanics from a system's time history or state and generalize to set expectations outside training domains. In spatio-temporal systems, it is also that shown local learning windows along space and time can provide improved model reliability in their approximation and generalization performance.

ISBN: 9781085562751Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Convolutional Neural Networks

Deep Learning in Engineering Mechanics: Wave Propagation and Dynamics Implementations.
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245 1 0 $a Deep Learning in Engineering Mechanics: Wave Propagation and Dynamics Implementations.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 79 p.
500 $a Source: Masters Abstracts International, Volume: 81-02.
500 $a Advisor: Srivastava, Ankit.
502 $a Thesis (M.S.)--Illinois Institute of Technology, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a With the advent of Artiﬁcial Intelligence research in the 1960s, the need for intelligent systems that are able to truly comprehend the physical world around them became relevant. Signiﬁcant milestones in the realm of machine learning and, in particular, deep learning during the past decade have led to advanced data-driven models that are able to approximate complex functions from pure observations. When it comes to the application of physics-based scenarios, the vast majority of these models rely on statistical and optimization constructs, leaving minimal room in their development for the physics-driven frameworks that more traditional engineering and science ﬁelds have been developing for centuries. On the other hand, the more traditional engineering ﬁelds, such as mechanics, have evolved on a diﬀerent set of modeling tools that are mostly based on physics driven assumptions and equations, typically aided by statistical tools for uncertainty handling. Deep learning models can provide signiﬁcant implementation advantages in commercial systems over traditional engineering modeling tools in the current economies of scale, but they tend to lack the strong reliability their counterparts naturally allow. The work presented in this thesis is aimed at assessing the potential of deep learning tools, such as Convolutional Neural Networks and Long Short-Term Memory Networks, as data-driven models in engineering mechanics, with a major focus on vibration problems. In particular, two implementation cases are presented: a data driven surrogate model to a Phononic eigenvalue problem, and a physics-learning model in rigid-body dynamics scenario. Through the applications presented, this work that shows select deep learning architectures can appropriately approximate complex functions found in engineering mechanics from a system's time history or state and generalize to set expectations outside training domains. In spatio-temporal systems, it is also that shown local learning windows along space and time can provide improved model reliability in their approximation and generalization performance.
590 $a School code: 0091.
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Mechanical engineering. $3 649730
650 4 $a Mechanics. $3 525881
653 $a Convolutional Neural Networks
653 $a Data-driven Mechanics
653 $a Data-driven Modeling
653 $a Deep Learning
653 $a Eigenvalue Problems
653 $a Machine learning
690 $a 0538
690 $a 0548
690 $a 0346
710 2 $a Illinois Institute of Technology. $b Mechanical, Materials and Aerospace Engineering. $3 3276529
773 0 $t Masters Abstracts International $g 81-02.
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791 $a M.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10981734