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Titlebook: Computational Mechanics with Deep Learning; An Introduction Genki Yagawa,Atsuya Oishi Textbook 2023 The Editor(s) (if applicable) and The A

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發(fā)表于 2025-3-21 18:53:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Mechanics with Deep Learning
副標(biāo)題An Introduction
編輯Genki Yagawa,Atsuya Oishi
視頻videohttp://file.papertrans.cn/233/232678/232678.mp4
概述Focuses on both computational mechanics and deep learning.Written in an easy-to-understand manner with detailed mathematical formulas.Include samples for practice
叢書名稱Lecture Notes on Numerical Methods in Engineering and Sciences
圖書封面Titlebook: Computational Mechanics with Deep Learning; An Introduction Genki Yagawa,Atsuya Oishi Textbook 2023 The Editor(s) (if applicable) and The A
描述.This book is intended for students, engineers, and researchers interested in both computational mechanics and deep learning. It presents the mathematical and computational foundations of Deep Learning with detailed mathematical formulas in an easy-to-understand manner. It also discusses various applications of Deep Learning in Computational Mechanics, with detailed explanations of the Computational Mechanics fundamentals selected there. Sample programs are included for the reader to try out in practice. This book is therefore useful for a wide range of readers interested in computational mechanics and deep learning..
出版日期Textbook 2023
關(guān)鍵詞Computational Mechanics; Deep Learning; Neural Networks; Machine Learning; Finite Element Method
版次1
doihttps://doi.org/10.1007/978-3-031-11847-0
isbn_softcover978-3-031-11849-4
isbn_ebook978-3-031-11847-0Series ISSN 1877-7341 Series E-ISSN 1877-735X
issn_series 1877-7341
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:14:48 | 只看該作者
Lecture Notes on Numerical Methods in Engineering and Scienceshttp://image.papertrans.cn/c/image/232678.jpg
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發(fā)表于 2025-3-22 02:12:07 | 只看該作者
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發(fā)表于 2025-3-22 05:44:34 | 只看該作者
978-3-031-11849-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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發(fā)表于 2025-3-22 11:41:12 | 只看該作者
https://doi.org/10.1007/978-1-349-03123-8eural network including the error back propagation algorithm, Sect.?. the convolutional neural networks, which have become the mainstream of deep learning in recent years, and Sect.?. compares various methods for accelerating the training process. Finally, Sect.?. describes regularization methods to
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發(fā)表于 2025-3-22 13:41:09 | 只看該作者
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發(fā)表于 2025-3-22 20:37:14 | 只看該作者
https://doi.org/10.1007/978-1-349-19936-5llision between objects is one of them. In this chapter, we study an application of deep learning to the contact search process, which is indispensable in contact and collision analysis. In particular, we focus on the contact between two smooth contact surfaces. In Sect.?., the basics of the contact
8#
發(fā)表于 2025-3-23 00:40:36 | 只看該作者
https://doi.org/10.1007/978-1-349-19936-5uss the application of deep learning to fluid dynamics problems. Section?. describes the basic equations of fluid dynamics, Sect.?. the basics of the finite difference method, one of the most popular methods for solving fluid dynamics problems, Sect.?. a practical example of a two-dimensional fluid
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發(fā)表于 2025-3-23 02:55:04 | 只看該作者
Organizing and Working in a Study Group,cy of element stiffness matrices (Sect.?.), finite element analysis using convolutional operations (Sect.?.), fluid analysis using variational autoencoders (Sect.?.), a zooming method using feedforward neural networks (Sect.?.), and an application of physics-informed neural networks to solid mechani
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發(fā)表于 2025-3-23 07:32:40 | 只看該作者
https://doi.org/10.1007/978-981-16-2305-9mputational Mechanics with Deep Learning” from the perspective of programming. Section . describes some programs in the field of computational mechanics used in the Data Preparation Phase, including three topics discussed in the case study: the element stiffness matrix by using numerical quadrature
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