Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems 1st Edition

Grabbing the next leap in computational science starts here. Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems, 1st Edition by Yinpeng Wang and Qiang Ren delivers a focused, modern roadmap for integrating neural networks with physics-based simulation and inverse problems.

This book bridges deep learning and computational physics, guiding readers through contemporary approaches to forward modeling and inversion. Clear explanations illuminate how machine learning accelerates the solution of partial differential equations, enhances parameter estimation, and improves stability in ill-posed problems. Practical examples and conceptual frameworks make complex topics accessible to researchers and practitioners alike.

Engineered for applied scientists, engineers, and graduate students worldwide, the text emphasizes real-world applications — from seismic imaging and geophysical exploration to medical imaging and computational fluid dynamics. Whether you’re developing fast surrogate models, designing data-driven inversion pipelines, or exploring physics-informed neural networks, this volume offers the methods and insights to advance your projects and publications.

Readable yet rigorous, the book balances mathematical foundations with algorithmic strategies and implementation-minded discussion, helping you translate theory into reproducible workflows. It’s an essential resource for anyone aiming to harness deep learning to improve forward solvers, reduce computational cost, and extract reliable subsurface or system parameters.

Enhance your toolkit with modern inversion and modeling techniques: add Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems to your library today — ideal for industry professionals, academics, and students pursuing cutting-edge computational research.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.