找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復制鏈接]
樓主: Anagram
31#
發(fā)表于 2025-3-26 22:35:33 | 只看該作者
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
32#
發(fā)表于 2025-3-27 01:31:16 | 只看該作者
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.
33#
發(fā)表于 2025-3-27 08:49:50 | 只看該作者
34#
發(fā)表于 2025-3-27 09:39:27 | 只看該作者
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.
35#
發(fā)表于 2025-3-27 16:28:24 | 只看該作者
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
36#
發(fā)表于 2025-3-27 18:46:34 | 只看該作者
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
37#
發(fā)表于 2025-3-27 22:15:03 | 只看該作者
38#
發(fā)表于 2025-3-28 05:29:42 | 只看該作者
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
39#
發(fā)表于 2025-3-28 09:53:06 | 只看該作者
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
40#
發(fā)表于 2025-3-28 14:17:18 | 只看該作者
 關于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經驗總結 SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2026-1-31 02:25
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
潢川县| 桂阳县| 宝坻区| 卓尼县| 苗栗县| 彝良县| 贡嘎县| 思茅市| 宜春市| 方城县| 郧西县| 象山县| 开平市| 南通市| 裕民县| 全州县| 武邑县| 崇州市| 铜陵市| 藁城市| 湛江市| 尼勒克县| 红原县| 肇源县| 青神县| 盘山县| 周口市| 温宿县| 辽宁省| 龙山县| 吴忠市| 呼玛县| 金堂县| 安新县| 南安市| 营山县| 娱乐| 定兴县| 灵寿县| 建平县| 都江堰市|