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

Wang, Yinpeng, Ren, Qiang

Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
  • 出版商: CRC
  • 出版日期: 2023-07-06
  • 售價: $3,530
  • 貴賓價: 9.5 折 $3,354
  • 語言: 英文
  • 頁數: 180
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032502983
  • ISBN-13: 9781032502984
  • 相關分類: DeepLearning、物理學 Physics

    • 下單後立即進貨 (約2~4週)

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商品描述

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.

商品描述(中文翻譯)

本書詳細探討了新興的深度學習（DL）技術在計算物理學中的應用，評估其有望取代傳統的數值求解器以實時計算場的潛力。在良好的訓練之後，所提出的架構可以解決正向計算和反向檢索問題。

本書從整體的角度出發，包括以下幾個方面。第一章討論了基本的DL框架。然後，在第二章中，使用經典的U-net解決了穩態熱傳導問題，包括被動和主動情況。接著，使用提出的Conv-LSTM重建了曲面上的複雜熱通量，展示了高精度和高效率。此外，在第四章中，使用物理知識的DL結構和非線性映射模塊，通過瞬態溫度獲取了與空間/溫度/時間相關的熱傳導率。最後，在第五章中，介紹了一系列最新的高級框架和相應的物理應用。

隨著深度學習技術在計算物理學中的蓬勃發展，越來越多的人希望有相關的閱讀材料。本書面向研究深度學習在計算物理學中應用的研究生、專業從業人員和研究人員。

作者簡介

Yinpeng Wang received the B.S. degree in Electronic and Information Engineering from Beihang University, Beijing, China in 2020, where he is currently pursuing his M.S. degree in Electronic Science and Technology. Mr. Wang focuses on the research of electromagnetic scattering, inverse scattering, heat transfer, computational multi-physical fields, and deep learning.

Qiang Ren received the B.S. and M.S. degrees both in electrical engineering from Beihang University, Beijing, China, and Institute of Acoustics, Chinese Academy of Sciences, Beijing, China in 2008 and 2011, respectively, and the PhD degree in Electrical Engineering from Duke University, Durham, NC, in 2015. From 2016 to 2017, he was a postdoctoral researcher with the Computational Electromagnetics and Antennas Research Laboratory (CEARL) of the Pennsylvania State University, University Park, PA. In September 2017, he joined the School of Electronics and Information Engineering, Beihang University as an "Excellent Hundred" Associate Professor.

作者簡介(中文翻譯)

Yinpeng Wang在2020年從北京航空航天大學獲得電子與信息工程學士學位，目前正在該校攻讀電子科學與技術碩士學位。王先生的研究方向包括電磁散射、反散射、熱傳遞、計算多物理場和深度學習。

Qiang Ren在2008年和2011年分別從北京航空航天大學和中國科學院聲學研究所獲得電氣工程學士和碩士學位，並在2015年從杜克大學獲得電氣工程博士學位。從2016年到2017年，他在賓夕法尼亞州立大學計算電磁學和天線研究實驗室（CEARL）擔任博士後研究員。2017年9月，他加入了北京航空航天大學電子與信息工程學院，擔任“優秀百人”副教授。

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