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.
Altre edizioni note dello stesso titolo
Risultati della ricerca per DEEP LEARN METHOD MATHE PHY (V1)
Foto dell'editore
DEEP LEARN METHOD MATHE PHY (V1)
Da: GreatBookPrices, Columbia, MD, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo 52888135-n
Compra nuovo
EUR 177,55
Spedizione EUR 2,24
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems (Hardcover)
Editore:
World Scientific Publishing Co Pte Ltd, Singapore, 2026
ISBN 10: 981982723X
ISBN 13: 9789819827237
Nuovo
Rilegato
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: new. 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. Codice articolo 9789819827237
Compra nuovo
EUR 179,87
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: 1 disponibili
Foto dell'editore
Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems
Editore:
World Scientific Publishing Co Pte Ltd, 2026
ISBN 10: 981982723X
ISBN 13: 9789819827237
Nuovo
Rilegato
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
Valutazione del venditore 5 su 5 stelle
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo CX-9789819827237
Compra nuovo
EUR 173,43
Spedizione EUR 6,78
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 15 disponibili
Foto dell'editore
Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems
Editore:
World Scientific Publishing Co Pte Ltd, 2026
ISBN 10: 981982723X
ISBN 13: 9789819827237
Nuovo
Rilegato
Da: PBShop.store US, Wood Dale, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo CX-9789819827237
Compra nuovo
EUR 181,27
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: 15 disponibili
Foto dell'editore
DEEP LEARN METHOD MATHE PHY (V1)
Da: GreatBookPrices, Columbia, MD, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: As New. Unread book in perfect condition. Codice articolo 52888135
Compra usato
EUR 188,25
Spedizione EUR 2,24
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
DEEP LEARN METHOD MATHE PHY (V1)
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo 52888135-n
Compra nuovo
EUR 173,42
Spedizione EUR 17,32
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: Più di 20 disponibili
Immagini fornite dal venditore
Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems
Editore:
World Scientific Publishing Co Pte Ltd, SG, 2026
ISBN 10: 981982723X
ISBN 13: 9789819827237
Nuovo
Rilegato
Da: Rarewaves.com USA, London, LONDO, Regno Unito
Valutazione del venditore 5 su 5 stelle
Hardback. Condizione: New. 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. Codice articolo LU-9789819827237
Compra nuovo
EUR 205,40
Spedizione gratuita
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 9 disponibili
Foto dell'editore
DEEP LEARN METHOD MATHE PHY (V1)
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: As New. Unread book in perfect condition. Codice articolo 52888135
Compra usato
EUR 188,53
Spedizione EUR 17,32
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems (Hardcover)
Editore:
World Scientific Publishing Co Pte Ltd, Singapore, 2026
ISBN 10: 981982723X
ISBN 13: 9789819827237
Nuovo
Rilegato
Da: AussieBookSeller, Truganina, VIC, Australia
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: new. 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9789819827237
Compra nuovo
EUR 191,97
Spedizione EUR 31,41
Spedito da Australia a U.S.A.
Spedito da Australia a U.S.A.
Quantità: 1 disponibili
Foto dell'editore
Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems
Editore:
World Scientific Publishing Co Pte Ltd, 2026
ISBN 10: 981982723X
ISBN 13: 9789819827237
Nuovo
Rilegato
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: Brand New. 550 pages. 6.00x1.19x9.00 inches. In Stock. Codice articolo x-981982723X
Compra nuovo
EUR 231,88
Spedizione EUR 14,44
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 2 disponibili