國立虎尾科技大學 |

Modern Deep Learning for Modeling Dynamical Systems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Modern Deep Learning for Modeling Dynamical Systems./
Author:	Geneva, Nicholas.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	255 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
Subject:	Computational physics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28968417
ISBN:	9798209887355

Modern Deep Learning for Modeling Dynamical Systems.
Geneva, Nicholas.

Modern Deep Learning for Modeling Dynamical Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 255 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--University of Notre Dame, 2022.

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

Advances in deep learning have made constructing, training and deploying deep neural networks more accessible than ever before. Due to their flexibility and predictive accuracy, neural networks have ushered in a new wave of data-driven and data-free modeling for physical phenomena. With several key research breakthroughs in the deep learning field, modern deep learning architectures are now more accurate and generalizable facilitating improved physics-informed models. This dissertation explores the use of several different deep learning approaches for learning physical dynamics including Bayesian neural networks, generative models, physics-constrained learning and self-attention. By leveraging these recent deep neural network advancements and probabilistic frameworks, powerful deep learning surrogates of physical systems can predict complex mutli-scale features.

ISBN: 9798209887355Subjects--Topical Terms:

1181955
Computational physics.
Subjects--Index Terms:

Bayesian neural networks

Modern Deep Learning for Modeling Dynamical Systems.
LDR:02094nam a2200397 4500 001 1104578
005 20230619080053.5
006 m o d
007 cr#unu||||||||
008 230907s2022 ||||||||||||||||| ||eng d
020 $a 9798209887355
035 $a (MiAaPQ)AAI28968417
035 $a AAI28968417
040 $a MiAaPQ $c MiAaPQ
100 1 $a Geneva, Nicholas. $0 (orcid)0000-0003-4562-459X $3 1413438
245 1 0 $a Modern Deep Learning for Modeling Dynamical Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 255 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
500 $a Advisor: Zabaras, Nicholas.
502 $a Thesis (Ph.D.)--University of Notre Dame, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Advances in deep learning have made constructing, training and deploying deep neural networks more accessible than ever before. Due to their flexibility and predictive accuracy, neural networks have ushered in a new wave of data-driven and data-free modeling for physical phenomena. With several key research breakthroughs in the deep learning field, modern deep learning architectures are now more accurate and generalizable facilitating improved physics-informed models. This dissertation explores the use of several different deep learning approaches for learning physical dynamics including Bayesian neural networks, generative models, physics-constrained learning and self-attention. By leveraging these recent deep neural network advancements and probabilistic frameworks, powerful deep learning surrogates of physical systems can predict complex mutli-scale features.
590 $a School code: 0165.
650 4 $a Computational physics. $3 1181955
650 4 $a Mechanical engineering. $3 557493
650 4 $a Artificial intelligence. $3 559380
653 $a Bayesian neural networks
653 $a Deep learning
653 $a Generative modeling
653 $a Physics-informed learning
653 $a Surrogate modeling
690 $a 0216
690 $a 0548
690 $a 0800
710 2 $a University of Notre Dame. $b Aerospace and Mechanical Engineering. $3 1413439
773 0 $t Dissertations Abstracts International $g 83-09B.
790 $a 0165
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28968417