Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Examines deep learning approaches for forward modeling and inversion tasks in computational physics, providing algorithmic and numerical insights for scientific applications.
About This Book
This book explores the application of deep learning techniques to forward modeling and inversion problems in computational physics.
It presents methods that integrate neural networks with traditional physics-based simulations.
Readers will find discussions on algorithmic frameworks and numerical approaches relevant to scientific computing.
The content is aimed at researchers and practitioners working at the intersection of machine learning and physics.
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