{"product_id":"deep-learningbased-forward-modeling-and-inversion-techniques-for-computational-physics-problems-9781032502984","title":"Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThis book explores the emerging deep learning technique in computational physics, assessing its potential to substitute conventional numerical solvers for real-time field calculations. It covers basic DL frameworks, solves heat conduction problems, reconstructs heat flux on curved surfaces, and introduces advanced frameworks and physics applications. It is intended for graduate students, professionals, and researchers interested in DL for computational physics. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 180 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 06 July 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis comprehensive book delves into the emerging field of deep learning (DL) in computational physics, exploring its remarkable potential to replace traditional numerical solvers for real-time field calculations. Through rigorous training, the proposed architecture demonstrates remarkable capabilities in both forward computing and inverse retrieval tasks.\u003cbr\u003e\u003cbr\u003eThe book encompasses a wide range of topics, beginning with an introduction to fundamental DL frameworks. In Chapter 2, the classical U-net is employed to solve the steady heat conduction problem, encompassing both passive and active scenarios. Chapter 3 showcases the reconstruction of sophisticated heat flux on curved surfaces using the Conv-LSTM, exhibiting exceptional accuracy and efficiency. Chapter 4 employs a physics-informed DL structure alongside a nonlinear mapping module to derive the space\/temperature\/time-related thermal conductivity through transient temperature measurements. Finally, Chapter 5 presents a comprehensive overview of the latest advanced frameworks and their corresponding physics applications.\u003cbr\u003e\u003cbr\u003eThis book is specifically designed for graduate students, professional practitioners, and researchers who are eager to explore the realm of DL for computational physics. Its detailed explanations, practical examples, and extensive references make it an invaluable resource for anyone seeking to advance their knowledge and expertise in this rapidly evolving field.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 434g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 162 x 241 x 17 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781032502984\u003c\/p\u003e","brand":"Yinpeng Wang,Qiang Ren","offers":[{"title":"Hardback","offer_id":44330601611514,"sku":"9781032502984","price":85.67,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1688745033236_book.jpg?v=1688975874","url":"https:\/\/shulphink.com\/products\/deep-learningbased-forward-modeling-and-inversion-techniques-for-computational-physics-problems-9781032502984","provider":"Shulph Ink","version":"1.0","type":"link"}