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

對手

Computational Mechanics with Deep Learning978-3-031-11847-0Series ISSN 1877-7341 Series E-ISSN 1877-735X

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Pillory

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 mechanics (Sect.?.).

紅腫

1877-7341 e samples for practice.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

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Outwit

Mathematical Background for Deep Learningning in recent years, and Sect.?. compares various methods for accelerating the training process. Finally, Sect.?. describes regularization methods to suppress overtraining for improving performance of the trained neural networks.

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Nausea

https://doi.org/10.1007/978-1-349-19936-5dynamics 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.

FLIC

https://doi.org/10.1007/978-1-349-19936-5h 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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