TY  - BOOK
AU  - Ray,Deep
AU  - Pinti,Orazio
AU  - Oberai,Assad A.
ED  - SpringerLink (Online service)
TI  - Deep Learning and Computational Physics
SN  - 9783031593451
AV  - TA345-345.5 
U1  - 620.00285 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Engineering
KW  - Data processing
KW  - Machine learning
KW  - Big data
KW  - Computational intelligence
KW  - Engineering mathematics
KW  - Mathematical physics
KW  - Data Engineering
KW  - Machine Learning
KW  - Big Data
KW  - Computational Intelligence
KW  - Mathematical and Computational Engineering Applications
KW  - Theoretical, Mathematical and Computational Physics
N1  - Introduction -- Introduction to deep neural networks -- Residual neural networks -- Convolutional Neural Networks -- Solving PDEs with Neural Networks -- Operator Networks -- Generative Deep Learning
N2  - 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.
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-59345-1
ER  -