找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

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

[复制链接]
楼主: Anagram
发表于 2025-3-25 03:43:41 | 显示全部楼层
Computational Mechanics with Deep Learning978-3-031-11847-0Series ISSN 1877-7341 Series E-ISSN 1877-735X
发表于 2025-3-25 10:50:00 | 显示全部楼层
发表于 2025-3-25 11:53:40 | 显示全部楼层
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. .).
发表于 2025-3-25 18:06:22 | 显示全部楼层
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
发表于 2025-3-25 21:49:04 | 显示全部楼层
发表于 2025-3-26 01:24:37 | 显示全部楼层
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.
发表于 2025-3-26 05:39:34 | 显示全部楼层
发表于 2025-3-26 11:01:58 | 显示全部楼层
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.
发表于 2025-3-26 16:33:22 | 显示全部楼层
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
发表于 2025-3-26 18:24:37 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-7-4 15:01
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表