Ray, Deep. creator author. aut http://id.loc.gov/vocabulary/relators/aut Pinti, Orazio. author. aut http://id.loc.gov/vocabulary/relators/aut Oberai, Assad A. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XVI, 152 p. 54 illus., 44 illus. in color. online resource. 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. . Introduction -- Introduction to deep neural networks -- Residual neural networks -- Convolutional Neural Networks -- Solving PDEs with Neural Networks -- Operator Networks -- Generative Deep Learning. by Deep Ray, Orazio Pinti, Assad A. Oberai. Engineering Data processing Machine learning Big data Computational intelligence Engineering mathematics Mathematical physics Data Engineering Machine Learning Big Data Computational Intelligence Mathematical and Computational Engineering Applications Theoretical, Mathematical and Computational Physics TA345-345.5 9783031593451 https://doi.org/10.1007/978-3-031-59345-1 https://doi.org/10.1007/978-3-031-59345-1 240606 20251030112302.0 978-3-031-59345-1