國立虎尾科技大學 |

Modern Deep Learning for Modeling Dynamical Systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modern Deep Learning for Modeling Dynamical Systems./
作者:	Geneva, Nicholas.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	255 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Artificial intelligence. -
電子資源:	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:

559380
Artificial intelligence.
Subjects--Index Terms:

Bayesian neural networks

Modern Deep Learning for Modeling Dynamical Systems.
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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.
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653 $a Deep learning
653 $a Generative modeling
653 $a Physics-informed learning
653 $a Surrogate modeling
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