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...
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| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton, FL
CRC Press
2024
|
| Edition: | First edition. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781000896671/?ar |
| 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. 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"-- |
| Item Description: | Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on August 01, 2023) |
| Physical Description: | 1 Online-Ressource (xiii, 185 Seiten) illustrations (some color) |
| ISBN: | 9781003397830 1003397832 9781000896671 1000896676 9781000896657 100089665X |
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| 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 | |
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| 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"-- | ||
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| author | Wang, Yinpeng 1999- Ren, Qiang |
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| id | ZDB-30-ORH-096166959 |
| illustrated | Illustrated |
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| spelling | Wang, Yinpeng 1999- VerfasserIn aut Deep learning-based forward modeling and inversion techniques for computational physics problems Yinpeng Wang, Qiang Ren First edition. Boca Raton, FL CRC Press 2024 ©2024 1 Online-Ressource (xiii, 185 Seiten) illustrations (some color) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on August 01, 2023) "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"-- Computational physics Physics Data processing Deep learning (Machine learning) Physique ; Informatique Apprentissage profond TECHNOLOGY / Electronics / General SCIENCE / Physics Physics ; Data processing Ren, Qiang VerfasserIn aut 9781032502984 Erscheint auch als Druck-Ausgabe 9781032502984 |
| spellingShingle | Wang, Yinpeng 1999- Ren, Qiang Deep learning-based forward modeling and inversion techniques for computational physics problems Computational physics Physics Data processing Deep learning (Machine learning) Physique ; Informatique Apprentissage profond TECHNOLOGY / Electronics / General SCIENCE / Physics Physics ; Data processing |
| title | Deep learning-based forward modeling and inversion techniques for computational physics problems |
| title_auth | Deep learning-based forward modeling and inversion techniques for computational physics problems |
| title_exact_search | Deep learning-based forward modeling and inversion techniques for computational physics problems |
| title_full | Deep learning-based forward modeling and inversion techniques for computational physics problems Yinpeng Wang, Qiang Ren |
| title_fullStr | Deep learning-based forward modeling and inversion techniques for computational physics problems Yinpeng Wang, Qiang Ren |
| title_full_unstemmed | Deep learning-based forward modeling and inversion techniques for computational physics problems Yinpeng Wang, Qiang Ren |
| title_short | Deep learning-based forward modeling and inversion techniques for computational physics problems |
| title_sort | deep learning based forward modeling and inversion techniques for computational physics problems |
| topic | Computational physics Physics Data processing Deep learning (Machine learning) Physique ; Informatique Apprentissage profond TECHNOLOGY / Electronics / General SCIENCE / Physics Physics ; Data processing |
| topic_facet | Computational physics Physics Data processing Deep learning (Machine learning) Physique ; Informatique Apprentissage profond TECHNOLOGY / Electronics / General SCIENCE / Physics Physics ; Data processing |
| work_keys_str_mv | AT wangyinpeng deeplearningbasedforwardmodelingandinversiontechniquesforcomputationalphysicsproblems AT renqiang deeplearningbasedforwardmodelingandinversiontechniquesforcomputationalphysicsproblems |