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

  • CategoryOther
  • TypeE-Books
  • LanguageEnglish
  • Total size532.6 MB
  • Uploaded Byfreecoursewb
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  • Last checkedJun. 06th '23
  • Date uploadedJun. 05th '23
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Infohash : D6FCFA3F739610383B026BFDF8F11F20408D710E

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



https://DevCourseWeb.com

English | 2023 | ISBN: 9781003397830 | 200 pages | True PDF | 532.57 MB

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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