找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Deep Learning and Computational Physics; Deep Ray,Orazio Pinti,Assad A. Oberai Textbook 2024 The Editor(s) (if applicable) and The Author(

[復(fù)制鏈接]
查看: 26896|回復(fù): 39
樓主
發(fā)表于 2025-3-21 18:41:00 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Deep Learning and Computational Physics
編輯Deep Ray,Orazio Pinti,Assad A. Oberai
視頻videohttp://file.papertrans.cn/265/264589/264589.mp4
概述Introduces a graduate student with linear algebra and partial differential equations to select topics in deep learning.Exploits the connections between deep learning algorithms and the more convention
圖書封面Titlebook: Deep Learning and Computational Physics;  Deep Ray,Orazio Pinti,Assad A. Oberai Textbook 2024 The Editor(s) (if applicable) and The Author(
描述.The main objective of this book is to introduce a student who is familiar with elementary math concepts to select topics in deep learning. It exploits strong connections between deep learning algorithms and the techniques of computational physics to achieve two important goals. First, it uses concepts from computational physics to develop an understanding of deep learning algorithms. Second, it describes several novel deep learning algorithms for solving challenging problems in computational physics, thereby offering someone who is interested in modeling physical phenomena with a complementary set of tools. It is intended for senior undergraduate and graduate students in science and engineering programs. It is used as a textbook for a course (or a course sequence) for senior-level undergraduate or graduate-level students.?.
出版日期Textbook 2024
關(guān)鍵詞Big Data; Deep Learning; Machine Learning; Computational Physics; Deep Learning Algorithms
版次1
doihttps://doi.org/10.1007/978-3-031-59345-1
isbn_softcover978-3-031-59347-5
isbn_ebook978-3-031-59345-1
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書目名稱Deep Learning and Computational Physics影響因子(影響力)




書目名稱Deep Learning and Computational Physics影響因子(影響力)學(xué)科排名




書目名稱Deep Learning and Computational Physics網(wǎng)絡(luò)公開度




書目名稱Deep Learning and Computational Physics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Learning and Computational Physics被引頻次




書目名稱Deep Learning and Computational Physics被引頻次學(xué)科排名




書目名稱Deep Learning and Computational Physics年度引用




書目名稱Deep Learning and Computational Physics年度引用學(xué)科排名




書目名稱Deep Learning and Computational Physics讀者反饋




書目名稱Deep Learning and Computational Physics讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒(méi)有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 21:35:48 | 只看該作者
板凳
發(fā)表于 2025-3-22 00:23:34 | 只看該作者
Textbook 2024y set of tools. It is intended for senior undergraduate and graduate students in science and engineering programs. It is used as a textbook for a course (or a course sequence) for senior-level undergraduate or graduate-level students.?.
地板
發(fā)表于 2025-3-22 07:06:24 | 只看該作者
https://doi.org/10.1007/978-3-030-23541-3tion and natural language processing. But the last few years have also witnessed the emergence of machine learning (in particular deep learning) algorithms to solve physics-driven problems, such as approximating solutions to partial differential equations and inverse problems.
5#
發(fā)表于 2025-3-22 09:51:34 | 只看該作者
Introduction,tion and natural language processing. But the last few years have also witnessed the emergence of machine learning (in particular deep learning) algorithms to solve physics-driven problems, such as approximating solutions to partial differential equations and inverse problems.
6#
發(fā)表于 2025-3-22 16:04:33 | 只看該作者
Textbook 2024s strong connections between deep learning algorithms and the techniques of computational physics to achieve two important goals. First, it uses concepts from computational physics to develop an understanding of deep learning algorithms. Second, it describes several novel deep learning algorithms fo
7#
發(fā)表于 2025-3-22 18:54:04 | 只看該作者
8#
發(fā)表于 2025-3-22 21:59:53 | 只看該作者
Siddhartha Asthana,Pushpendra Singhly used methods include finite difference/volume methods, finite element methods, spectral Galerkin methods, and also deep neural networks! To better appreciate some of these methods, especially deep neural networks, let us consider a simple model problem describing the scalar advection-diffusion problem in one-dimension.
9#
發(fā)表于 2025-3-23 03:45:43 | 只看該作者
10#
發(fā)表于 2025-3-23 08:30:57 | 只看該作者
nterested in modeling physical phenomena with a complementary set of tools. It is intended for senior undergraduate and graduate students in science and engineering programs. It is used as a textbook for a course (or a course sequence) for senior-level undergraduate or graduate-level students.?.978-3-031-59347-5978-3-031-59345-1
 關(guān)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(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-14 20:39
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
金湖县| 金阳县| 安西县| 长宁区| 五大连池市| 商水县| 依兰县| 乐昌市| 青神县| 永仁县| 保山市| 雷山县| 萨嘎县| 营山县| 云梦县| 琼海市| 白玉县| 黄冈市| 汾阳市| 松阳县| 广西| 通江县| 亳州市| 榆中县| 绵阳市| 乐平市| 基隆市| 汉寿县| 盘山县| 高陵县| 杭锦旗| 波密县| 娱乐| 新乡市| 兴城市| 治县。| 石柱| 安化县| 北流市| 朔州市| 襄樊市|