对手
发表于 2025-3-25 03:43:41
Computational Mechanics with Deep Learning978-3-031-11847-0Series ISSN 1877-7341 Series E-ISSN 1877-735X
不满分子
发表于 2025-3-25 10:50:00
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Pillory
发表于 2025-3-25 11:53:40
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. .).
红肿
发表于 2025-3-25 18:06:22
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
多样
发表于 2025-3-25 21:49:04
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Outwit
发表于 2025-3-26 01:24:37
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.
bromide
发表于 2025-3-26 05:39:34
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Nausea
发表于 2025-3-26 11:01:58
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
发表于 2025-3-26 16:33:22
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
–DOX
发表于 2025-3-26 18:24:37
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