机读格式显示(MARC)
- 000 02955cam a2200409 i 4500
- 008 230329s2024 flua b 001 0 eng
- 015 __ |a GBC392318 |2 bnb
- 016 7_ |a 021053631 |2 Uk
- 020 __ |a 9781032502984 |q (hardcover)
- 020 __ |a 1032502983 |q (hardcover)
- 020 __ |a 9781032503035 |q (paperback)
- 020 __ |a 1032503033 |q (paperback)
- 020 __ |z 9781003397830 |q (ebook)
- 020 __ |z 9781000896671 |q (ePub ebook)
- 020 __ |z 9781000896657 |q (PDF ebook)
- 040 __ |a DLC |b eng |e rda |c DLC |d UKMGB |d OCLCF |d OCLCQ |d OCLCO
- 050 00 |a QC52 |b .W36 2023
- 082 00 |a 530.0285/631 |2 23/eng20230512
- 100 1_ |a Wang, Yinpeng, |d 1999- |e author.
- 245 10 |a Deep learning-based forward modeling and inversion techniques for computational physics problems / |c Yinpeng Wang and Qiang Ren.
- 260 __ |a Boca Raton : |b CRC Press, |c 2024.
- 300 __ |a xiii, 185 pages : |b illustrations ; |c 24 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |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.
- 650 _0 |a Computational physics.
- 650 _0 |a Physics |x Data processing.
- 650 _0 |a Deep learning (Machine learning)
- 700 1_ |a Ren, Qiang |c (Associate professor), |e author.