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

Calin, Ovidiu

 
9789819827923: Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems
Brossura
ISBN 10: 9819827922 ISBN 13: 9789819827923
Casa editrice: WSPC, 2026

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Editore
WSPC
Data di pubblicazione
2026
Lingua
Inglese
ISBN 10 
9819827922
ISBN 13 
9789819827923
Rilegatura
Copertina flessibile
Numero di pagine
552
Contatto del produttore
non disponibile
Persona responsabile
gpsr@libri.de
gpsr@libri.de

Friedensallee 273
Hamburg
22763
Germania

Altre edizioni note dello stesso titolo

9789819827237: DEEP LEARN METHOD MATHE PHY (V1)

Edizione in evidenza

ISBN 10:  981982723X ISBN 13:  9789819827237
Casa editrice: World Scientific, 2026
Rilegato