Deep Learning Methods Of Mathematical Physics - Volume I Direct And Inverse Problems
Category: eBooks
- views: 207
- date: 7 June 2026
- posted by: BeMyLove
Deep Learning Methods Of Mathematical PhysicVolume I Direct And Inverse Problems | 25.31 MB
Title: Deep Learning Methods of Mathematical PhysicVolume I: Direct and Inverse Problems (552 Pages)
Author: Ovidiu Calin
Category: Nonfiction, Science & Nature, Science, Physics, Mathematical Physics, Computers, Advanced Computing, Artificial Intelligence
Language: English | 553 Pages | ISBN: 9819827922
Description:
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.
Readership: Advanced undergraduate and graduate students, researchers and practitioners in the fields of AI, Mathematical Physics, Computer Science, and Engineering.
DOWNLOAD:
https://rapidgator.net/file/26610149fe7a248866636091ce6c4624/Deep_Learning_Methods_Of_Mathematical_Physics_-_Volume_I_Direct_And_Inverse_Problems.rar
https://nitroflare.com/view/A03CD71AA7BC438/Deep_Learning_Methods_Of_Mathematical_Physics_-_Volume_I_Direct_And_Inverse_Problems.rar
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.
Readership: Advanced undergraduate and graduate students, researchers and practitioners in the fields of AI, Mathematical Physics, Computer Science, and Engineering.
DOWNLOAD:
https://rapidgator.net/file/26610149fe7a248866636091ce6c4624/Deep_Learning_Methods_Of_Mathematical_Physics_-_Volume_I_Direct_And_Inverse_Problems.rar
https://nitroflare.com/view/A03CD71AA7BC438/Deep_Learning_Methods_Of_Mathematical_Physics_-_Volume_I_Direct_And_Inverse_Problems.rar
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||