Custom cover image
Custom cover image

Deep Learning and Computational Physics / by Deep Ray, Orazio Pinti, Assad A. Oberai

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Cham : Springer Nature Switzerland, 2024Publisher: Cham : Imprint: Springer, 2024Edition: 1st ed. 2024Description: 1 Online-Ressource(XVI, 152 p. 49 illus., 42 illus. in color.)ISBN:
  • 9783031593451
Subject(s): Additional physical formats: 9783031593444 | 9783031593468 | 9783031593475 | Erscheint auch als: 9783031593444 Druck-Ausgabe | Erscheint auch als: 9783031593468 Druck-Ausgabe | Erscheint auch als: 9783031593475 Druck-AusgabeDDC classification:
  • 62,000,285 23
  • 620.00285 23
DOI: DOI: 10.1007/978-3-031-59345-1Online resources: Summary: Introduction -- Introduction to deep neural networks -- Residual neural networks -- Convolutional Neural Networks -- Solving PDEs with Neural Networks -- Operator Networks -- Generative Deep Learning.Summary: 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. .PPN: PPN: 1891054821Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT
No physical items for this record
KIT – The University in the Helmholtz Association                         Home | Impressum | Datenschutz | Barrierefreiheit | KIT