Deep learning and computational physics
Introduction.- Introduction to deep neural networks.- Residual neural networks.- Convolutional Neural Networks.- Solving PDEs with Neural Networks.- Operator Networks.- Generative Deep Learning.
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| Weitere Verfasser: | , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2024
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| Schlagworte: |
Artificial intelligence
> COM094000
> COMPUTERS / Artificial Intelligence
> COMPUTERS / Database Management / General
> Databases
> Datenbanken
> Künstliche Intelligenz
> MATHEMATICS / Applied
> Machine learning
> Maschinelles Lernen
> Mathematical physics
> Mathematik für Ingenieure
> Mathematische Physik
> Maths for engineers
> SCIENCE / Mathematical Physics
> Computerphysik
> Deep learning
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| Online Zugang: | Cover |
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| Zusammenfassung: | 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 |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xvi, 152 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031593444 978-3-031-59344-4 |