Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems - Tapa blanda

Calin, Ovidiu

 
9789819827923: Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems
Tapa blanda
ISBN 10: 9819827922 ISBN 13: 9789819827923
Editorial: WSPC, 2026

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Editorial
WSPC
Año de publicación
2026
Idioma
Inglés
ISBN 10 
9819827922
ISBN 13 
9789819827923
Encuadernación
Tapa blanda
Número de páginas
552
Contacto del fabricante
no disponible
Persona responsable
gpsr@libri.de
gpsr@libri.de

Friedensallee 273
Hamburg
22763
Alemania

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ISBN 10:  981982723X ISBN 13:  9789819827237
Editorial: World Scientific, 2026
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