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题名/责任者:

Deep learning-based forward modeling and inversion techniques for computational physics problems / Yinpeng Wang and Qiang Ren.

版本说明:

First edition.

出版发行项:

Boca Raton : CRC Press, 2024.

ISBN:

9781032502984

ISBN:

1032502983

ISBN:

9781032503035

ISBN:

1032503033

载体形态项:

xiii, 185 pages : illustrations ; 24 cm

个人责任者:

Wang, Yinpeng, 1999- author.

附加个人名称:

Ren, Qiang (Associate professor), author.

论题主题:

Computational physics.

论题主题:

Physics-Data processing.

论题主题:

Deep learning (Machine learning)

中图法分类号:

O411.1

书目附注:

Includes bibliographical references and index.

摘要附注:

"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. Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Besides, the electromagnetic parameters of complex medium such as the permittivity and conductivity are retrieved by a cascaded framework in Chapter 4. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 5. Finally, in Chapter 6, a series of the latest advanced frameworks and the corresponding physics applications are introduced. As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics"--

豆瓣简介：