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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems

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

Wang, Yinpeng
Ren, Qiang

Paperback

Technology & EngineeringGeneral ComputersPhysics

ISBN10: 1032503033
ISBN13: 9781032503035
Publisher: CRC Press
Published: Jan 30 2025
Pages: 180
Weight: 0.63
Height: 0.42 Width: 6.14 Depth: 9.21
Language: English

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.

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