Deep Learning Methods Mathematical by Calin Ovidiu (23 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: New.

Paperback. Condition: 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.…

Paperback. Condition: Brand New. 552 pages. 6.00x1.25x9.00 inches. In Stock.

Paperback. Condition: Brand New. 492 pages. 6.00x1.11x9.00 inches. In Stock.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: 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.…

Condition: New.

Hardback. Condition: 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.…

Hardcover. Condition: Brand New. 550 pages. 6.00x1.19x9.00 inches. In Stock.

Hardcover. Condition: Brand New. 492 pages. 6.24x1.35x9.24 inches. In Stock.

Hardback. Condition: 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.…

Paperback. Condition: new. Paperback. 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 multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condition: new. Paperback. 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 UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Paperback. Condition: new. Paperback. 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. 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. …

Hardcover. Condition: new. 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 multiple locations in the US or from the UK, depending on stock availability. …

Hardcover. Condition: new. 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 UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Hardcover. Condition: 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condition: new. 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. …