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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Contact Mechanics with Deep Learningh as segmentation of NURBS-defined shapes, and conventional surface-to-surface contact search methods are taken, respectively. With these preparations, Sect. . formulates a contact search method using deep learning, and finally, Sect. . shows a numerical example

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Bases for Computer Programmingscusses some programs in C and Python for deep learning (neural networks) used in the Training Phase, where the mathematical formulas are described in detail so that they can be easily compared with practical programs.

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Flow Simulation with Deep Learningdynamics simulation, Sect. . the formulation of the application of deep learning to fluid dynamics problems, Sect. . recurrent neural networks that are suitable for the time-dependent problems covered in this chapter, and finally, Sect. . a real application of deep learning to the fluid dynamics simulation.

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1877-7341 lected 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..978-3-031-11849-4978-3-031-11847-0Series ISSN 1877-7341 Series E-ISSN 1877-735X

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Mathematical Background for Deep Learningeural 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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Contact Mechanics with Deep Learningllision 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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Flow Simulation with Deep Learninguss 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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