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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书目名称Computational Mechanics with Deep Learning影响因子(影响力)




书目名称Computational Mechanics with Deep Learning影响因子(影响力)学科排名




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书目名称Computational Mechanics with Deep Learning网络公开度学科排名




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书目名称Computational Mechanics with Deep Learning被引频次学科排名




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书目名称Computational Mechanics with Deep Learning年度引用学科排名




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书目名称Computational Mechanics with Deep Learning读者反馈学科排名

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Lecture Notes on Numerical Methods in Engineering and Scienceshttp://image.papertrans.cn/c/image/232678.jpg

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978-3-031-11849-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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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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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

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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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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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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