
Deep Learning and Computational Physics
by: Deep Ray (Author),Orazio Pinti(Author),Assad A. Oberai(Author)&0more
Publisher: Springer
Edition: 2024th
Publication Date: 2024/6/7
Language: English
Print Length: 168 pages
ISBN-10: 3031593448
ISBN-13: 9783031593444
Book Description
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.
About the Author
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. Read more
Deep Learning and Computational Physics
未经允许不得转载:电子书百科大全 » Deep Learning and Computational Physics
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