DEEP LEARNING METHODS OF MATHEMATICAL PHYSICS - VOLUME II: HOLONOMIC, NONHOLONOMIC AND STOCHASTIC DYNAMICS (Hardcover)

Language: English

Published by World Scientific Publishing Co Pte Ltd, Singapore, 2026

9819839122 / 9789819839124

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Hardcover. This book explores how Artificial Intelligence and Deep Learning are advancing Mathematical Physics by providing powerful computational tools for systems governed by geometric constraints, stochastic processes, thermodynamics, and complex differential equations. Extending the foundations established in Volume I, it presents modern data-driven and physics-informed approaches for modeling nonlinear dynamics, discovering hidden structures, and solving challenging direct and inverse problems where classical methods become computationally demanding.This book introduces manifold and distribution learning, Physics-Informed Neural Networks (PINNs), variational and structure-preserving neural networks, and thermodynamics-informed models, demonstrating how they preserve geometric and physical principles while solving complex mathematical problems. Covering holonomic and nonholonomic mechanics, Hamiltonian systems, chemical kinetics, generalized solutions of differential equations, and stochastic dynamical systems, it combines mathematical theory with computational experiments, Keras code examples, Google Colab notebooks, and practical exercises. Serving as both an introduction to emerging research directions and a hands-on guide, this book is intended for graduate students, researchers, and practitioners in mathematics, physics, engineering, and computer science seeking advanced applications of Deep Learning in Mathematical Physics. 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.…

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Title
DEEP LEARNING METHODS OF MATHEMATICAL PHYSICS - VOLUME II: HOLONOMIC, NONHOLONOMIC AND STOCHASTIC DYNAMICS (Hardcover)
Author
Ovidiu Calin
Publisher
World Scientific Publishing Co Pte Ltd, Singapore
Publication year
2026
Condition
new
Binding
Hardcover
Language
English
ISBN 10
9819839122
ISBN 13
9789819839124

AussieBookSeller

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