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DEEP LEARN METHOD MATHE PHY (V1)

Language EnglishEnglish
Book Paperback
Book DEEP LEARN METHOD MATHE PHY (V1) CALIN OVIDIU
Libristo code: 51430762
Publishers World Scientific Publishing Co Pte Ltd, March 2026
This book explores how Artificial Intelligence and Deep Learning are transforming Mathematical Physi... Full description
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92.32 €
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This book explores how Artificial Intelligence and Deep Learning are transforming Mathematical Physics, offering modern data-driven tools where traditional analytical and numerical methods fall short. As physical systems grow more complex or chaotic, deep learning provides efficient surrogates and physics-informed models capable of capturing dynamics and uncovering governing laws directly from data.

This book introduces Neural ODEs, Physics-Informed Neural Networks (PINNs), and Hamiltonian and Lagrangian Neural Networks, showing how they enhance classical mechanics and PDE solvers for both forward and inverse problems. With Keras code examples, Google Colab notebooks, and practical exercises, this book serves researchers and students in physics, mathematics, and engineering seeking a concise, hands-on guide to applying deep learning in physical systems.

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About the book

Full name DEEP LEARN METHOD MATHE PHY (V1)
Author CALIN OVIDIU
Language English
Binding Book - Paperback
Date of issue 2026
Number of pages 554
EAN 9789819827923
ISBN 9819827922
Libristo code 51430762
Weight 733
Dimensions 152 x 229 x 29
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