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

Af: Yinpeng Wang, Qiang Ren Engelsk Hardback

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

Af: Yinpeng Wang, Qiang Ren Engelsk Hardback
Tjek vores konkurrenters priser

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. 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 4. Finally, in Chapter 5, 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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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. 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 4. Finally, in Chapter 5, 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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 180
ISBN-13: 9781032502984
Indbinding: Hardback
Udgave:
ISBN-10: 1032502983
Kategori: Numerisk analyse
Udg. Dato: 6 jul 2023
Længde: 17mm
Bredde: 241mm
Højde: 162mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 6 jul 2023
Forfatter(e): Yinpeng Wang, Qiang Ren
Forfatter(e) Yinpeng Wang, Qiang Ren

Kategori Numerisk analyse

ISBN-13 9781032502984

Sprog Engelsk

Indbinding Hardback

Sider 180

Udgave

Længde 17mm

Bredde 241mm

Højde 162mm

Udg. Dato 6 jul 2023

Oplagsdato 6 jul 2023

Forlag Taylor & Francis Ltd

Kategori sammenhænge