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Deep learning-based forward modeling and inversion techniques for computational physics problems /
"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 forwa...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
2024.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Wang, Yinpeng, |d 1999- |e author. | |
| 245 | 1 | 0 | |a Deep learning-based forward modeling and inversion techniques for computational physics problems / |c Yinpeng Wang, Qiang Ren. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c 2024. | |
| 264 | 4 | |c Ã2024 | |
| 300 | |a 1 online resource (xiii, 185 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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"-- |c Provided by publisher. | ||
| 545 | 0 | |a Yinpeng Wang received the B.S. degree in Electronic and Information Engineering from Beihang University, Beijing, China in 2020, where he is currently pursuing his M.S. degree in Electronic Science and Technology. Mr. Wang focuses on the research of electromagnetic scattering, inverse scattering, heat transfer, computational multi-physical fields, and deep learning. Qiang Ren received the B.S. and M.S. degrees both in electrical engineering from Beihang University, Beijing, China, and Institute of Acoustics, Chinese Academy of Sciences, Beijing, China in 2008 and 2011, respectively, and the PhD degree in Electrical Engineering from Duke University, Durham, NC, in 2015. From 2016 to 2017, he was a postdoctoral researcher with the Computational Electromagnetics and Antennas Research Laboratory (CEARL) of the Pennsylvania State University, University Park, PA. In September 2017, he joined the School of Electronics and Information Engineering, Beihang University as an "Excellent Hundred" Associate Professor. | |
| 588 | |a Description based on online resource; title from digital title page (viewed on August 01, 2023). | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Computational physics. | |
| 650 | 0 | |a Physics |x Data processing. | |
| 650 | 0 | |a Deep learning (Machine learning) | |
| 650 | 6 | |a Physique |x Informatique. | |
| 650 | 6 | |a Apprentissage profond. | |
| 650 | 7 | |a TECHNOLOGY / Electronics / General |2 bisacsh | |
| 650 | 7 | |a SCIENCE / Physics |2 bisacsh | |
| 650 | 7 | |a Deep learning (Machine learning) |2 fast | |
| 650 | 7 | |a Physics |x Data processing |2 fast | |
| 700 | 1 | |a Ren, Qiang |c (Associate professor), |e author. | |
| 776 | 0 | 8 | |i Print version: |a Wang, Yinpeng, 1999- |t Deep learning-based forward modeling and inversion techniques for computational physics problems |b First edition. |d Boca Raton : CRC Press, 2023 |z 9781032502984 |w (DLC) 2022060811 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781000896671/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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