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.
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.
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