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Deep learning and computational physics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning and computational physicsby Deep Ray, Orazio Pinti, Assad A. Oberai.
作者:	Ray, Deep.
其他作者:	Pinti, Orazio.
出版者:	Cham :Springer Nature Switzerland :2024.
面頁冊數:	xvi, 152 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning)
電子資源:	https://doi.org/10.1007/978-3-031-59345-1
ISBN:	9783031593451$q(electronic bk.)

Deep learning and computational physics
Ray, Deep.

Deep learning and computational physics[electronic resource] /by Deep Ray, Orazio Pinti, Assad A. Oberai. - Cham :Springer Nature Switzerland :2024. - xvi, 152 p. :ill., digital ;24 cm.

Introduction -- Introduction to deep neural networks -- Residual neural networks -- Convolutional Neural Networks -- Solving PDEs with Neural Networks -- Operator Networks -- Generative Deep Learning.

The main objective of this book is to introduce a student who is familiar with elementary math concepts to select topics in deep learning. It exploits strong connections between deep learning algorithms and the techniques of computational physics to achieve two important goals. First, it uses concepts from computational physics to develop an understanding of deep learning algorithms. Second, it describes several novel deep learning algorithms for solving challenging problems in computational physics, thereby offering someone who is interested in modeling physical phenomena with a complementary set of tools. It is intended for senior undergraduate and graduate students in science and engineering programs. It is used as a textbook for a course (or a course sequence) for senior-level undergraduate or graduate-level students.

ISBN: 9783031593451$q(electronic bk.)

Standard No.: 10.1007/978-3-031-59345-1doiSubjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning and computational physics
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520 $a The main objective of this book is to introduce a student who is familiar with elementary math concepts to select topics in deep learning. It exploits strong connections between deep learning algorithms and the techniques of computational physics to achieve two important goals. First, it uses concepts from computational physics to develop an understanding of deep learning algorithms. Second, it describes several novel deep learning algorithms for solving challenging problems in computational physics, thereby offering someone who is interested in modeling physical phenomena with a complementary set of tools. It is intended for senior undergraduate and graduate students in science and engineering programs. It is used as a textbook for a course (or a course sequence) for senior-level undergraduate or graduate-level students.
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