Deep Learning Methods of Mathematical Physics vI by Calin Ovidiu(.PDF)
File Size: 25.0 MB
Deep Learning Methods of Mathematical Physics – Volume I: Direct and Inverse Problems by Calin Ovidiu
Requirements: .PDF reader, 25.0 MB | True PDF
Overview: 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. Keras is used throughout because it is widely adopted, well documented, and well supported within the TensorFlow ecosystem. Prior experience with Python, TensorFlow, or Keras is helpful but not required; the book provides ample examples and guidance to support implementation.
Genre: Non-Fiction > Tech & Devices
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