DEEP LEARNING METHODS OF MATHEMATICAL PHYSICS - VOLUME I: DIRECT AND INVERSE PROBLEMS (Hardcover)

Language: English

Published by World Scientific Publishing Co Pte Ltd, Singapore, 2026

981982723X / 9789819827237

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Hardcover. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Title
DEEP LEARNING METHODS OF MATHEMATICAL PHYSICS - VOLUME I: DIRECT AND INVERSE PROBLEMS (Hardcover)
Author
Ovidiu Calin
Publisher
World Scientific Publishing Co Pte Ltd, Singapore
Publication year
2026
Condition
new
Binding
Hardcover
Language
English
ISBN 10
981982723X
ISBN 13
9789819827237
Edition
3rd Edition

Grand Eagle Retail

Bensenville, IL, U.S.A.

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