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Modern Deep Learning for Modeling Dynamical Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modern Deep Learning for Modeling Dynamical Systems./
作者:	Geneva, Nicholas.
面頁冊數:	1 online resource (255 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Computational physics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28968417click for full text (PQDT)
ISBN:	9798209887355

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

Modern Deep Learning for Modeling Dynamical Systems. - 1 online resource (255 pages)

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

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

Includes bibliographical references

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.

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

Mode of access: World Wide Web

ISBN: 9798209887355Subjects--Topical Terms:

3343998
Computational physics.
Subjects--Index Terms:

Bayesian neural networksIndex Terms--Genre/Form:

542853
Electronic books.

Modern Deep Learning for Modeling Dynamical Systems.
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504 $a Includes bibliographical references
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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538 $a Mode of access: World Wide Web
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653 $a Physics-informed learning
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