Deep Learning Based Forward Modeling and Inversion Techniques for - Computational Physics Problem

List of contents

1. Deep Learning Framework and Paradigm in Computational Physics   2. Application of U-net in 3D Steady Heat Conduction Solver  3. Inversion of complex surface heat flux based on ConvLSTM  4. Time-domain electromagnetic inverse scattering based on deep learning  5. Reconstruction of thermophysical parameters based on deep learning  6. Advanced Deep Learning Techniques in Computational Physics 

Summary

This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.

Product details

• Authors: Qiang Ren, Ren Qiang, Yinpeng Wang, Yinpeng Ren Wang
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 06.07.2023
• EAN: 9781032502984
• ISBN: 978-1-0-3250298-4
• No. of pages: 180
• Subjects: Non-fiction book > Nature, technology > Astronomy: general, reference works; machine learning, SCIENCE / Physics / General, Physics, COMPUTERS / Data Science / Machine Learning