Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
暫譯: 基於深度學習的前向建模與反演技術於計算物理問題中的應用
Wang, Yinpeng, Ren, Qiang
- 出版商: CRC
- 出版日期: 2025-01-30
- 售價: $2,360
- 貴賓價: 9.5 折 $2,242
- 語言: 英文
- 頁數: 180
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1032503033
- ISBN-13: 9781032503035
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相關分類:
DeepLearning、物理學 Physics
海外代購書籍(需單獨結帳)
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)
這本書詳細探討了在計算物理中新興的深度學習（DL）技術，評估其在實時計算場方面替代傳統數值解算器的潛力。在良好的訓練之後，所提出的架構可以解決前向計算和反向檢索問題。
從整體的角度出發，本書涵蓋了以下幾個領域。第一章討論了基本的深度學習框架。接著，在第二章中，通過經典的 U-net 解決穩態熱傳導問題，涉及被動和主動兩種情況。之後，利用所提出的 Conv-LSTM 重建曲面上的複雜熱流，展現出高準確性和效率。此外，在第四章中，採用物理知識驅動的深度學習結構以及非線性映射模組，通過瞬態溫度獲得與空間/溫度/時間相關的熱導率。最後，在第五章中，介紹了一系列最新的先進框架及其相應的物理應用。
隨著深度學習技術在計算物理中蓬勃發展，越來越多的人渴望相關的閱讀材料。本書旨在為對計算物理中的深度學習感興趣的研究生、專業從業者和研究人員提供參考。
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)
王寅鵬於2020年獲得中國北京的北京航空航天大學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。王先生專注於電磁散射、逆散射、熱傳遞、計算多物理場及深度學習的研究。
任強於2008年和2011年分別獲得中國北京的北京航空航天大學和中國科學院聲學研究所的電氣工程學士及碩士學位，並於2015年在美國杜克大學獲得電氣工程博士學位。2016年至2017年，他在美國賓夕法尼亞州立大學計算電磁學與天線研究實驗室（CEARL）擔任博士後研究員。2017年9月，他加入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。
