| 查看: 314 | 回复: 3 | |||
| 【奖励】 本帖被评价1次，作者huishouzhong增加金币 0.4 个 | |||
[资源]
Machine Learning in Modeling and Simulation - Methods Applications
|
|||
|
分享计算力学大牛klaus-jürgen bathe和timon rabczuk在2023年新编的书： machine learning in modeling and simulation methods and applications  两位大牛的引用次数：  ---------  前言节选： our objective in this book is to present ml techniques for computer-aided engineering with a focus on the fundamental theoretical ingredients and the exciting use of ml in the next generation of computational methods for modeling and simulation. an impressive example of ml in engineering is the emergence and great potential of using digital twins for design and monitoring of structures, with the word “structures” interpreted to include not only traditional structures, like buildings, dams, and bridges, but also biological, offshore, electromagnetic, turbines, nuclear, and many other structures. we foresee here major and widely spread applications. the book consists of 12 chapters focusing on machine learning in modeling and simulation, starting with an extensive overview of concepts and applications in chap. 1. the following two chapters provide a historical and theoretical overview—including implementation details—of two very popular machine learning approaches: artificial neural networks and gaussian processes. the remainder of the book focuses on the use of ml techniques to solve specific problems in engineering, physics or materials science starting with data-driven model discovery followed by physics-informed neural networks that may become a powerful alternative to classical discretization methods such as finite elements. namely, the networks allow the solution of partial differential equations while also incorporating experimentaldata. physics-informeddeepneuraloperatorscanbeseenasanimprovement and have the potential of solving not only one specific problem but any problem in a large class of problems. the next chapters focus on important topics regarding digital twins, reduced order modeling, regression models and the valuable use of ml in topology optimization methodologies. the last two chapters of the book focus on the design of new materials. the first approach is data-driven while the second approach takes advantage of interatomic potentials in the context of hierarchical multiscale modeling. these ml approaches have the potential to significantly widen the possibilities and accelerate the design of new materials. the work on this book has been very exciting, and we greatly thank all authors and co-authors of the book chapters. their valuable contributions, dedicated work, and great cooperation made it possible to complete this book in a timely manner. weimar, germany timon rabczuk cambridge, usa klaus-jürgen bathe december 2022 |
回复此楼» 本帖附件资源列表
-
欢迎监督和反馈：小木虫仅提供交流平台，不对该内容负责。
本内容由用户自主发布，如果其内容涉及到知识产权问题，其责任在于用户本人，如对版权有异议，请联系邮箱：libolin3@tal.com - 附件 1 : 2023_Machine_Learning_in_Modeling_and_Simulation.pdf
2024-04-15 15:53:25, 12.76 M
» 收录本帖的淘贴专辑推荐
 数值仿真算法书籍 |
» 本帖已获得的红花（最新10朵）
» 猜你喜欢
论大家对6070后普通教授导师的看法
已经有7人回复
-
招聘催化、储能博士
已经有12人回复
-
24年博士招生
已经有7人回复
-
真急着毕业，CPB主编终审17天了，邮件催稿了两次，就是一点动静没有
已经有6人回复
-
九部门发文：不得将专利授权数量作为人才评价、项目评审、职称评定、高校评价等的条件
已经有12人回复
-
高手朋友推荐比较容易投稿和录用的SCI期刊，不投稿收费SC，对分区没有要求
已经有4人回复
-
每到中夜，情难自抑
已经有38人回复
-
纠结选哪一个期刊，电化学领域
已经有3人回复
-
入职高校3年发表10+SCI，尽人事听天命
已经有34人回复
-
materials letter
已经有5人回复
4楼2024-05-09 09:12:42
简单回复
cl12072楼
2024-04-18 11:24
回复
一般 顶一下，感谢分享！
cl12073楼
2024-04-18 11:28
回复
送红花一朵
5
