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发表于 2025-3-21 17:58:46
书目名称Deep Learning in Computational Mechanics影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0264618<br><br> <br><br>书目名称Deep Learning in Computational Mechanics读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0264618<br><br> <br><br>
清唱剧
发表于 2025-3-21 20:39:32
C. Z. Dong,P. Nordlander,T. E. Madeyhysics and engineering have also taken advantage of machine learning by tuning these methods for their purpose. This chapter starts with a general review and then describes combined models and surrogate models. The idea is to show how machine learning can be used in physics and engineering without diving into technical details.
占线
发表于 2025-3-22 02:14:32
https://doi.org/10.1007/978-3-030-76587-3Computational Intelligence; Artificial Intelligence; Computational Mechanics; Neural Networks; Machine L
奇思怪想
发表于 2025-3-22 07:40:18
978-3-030-76589-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
ARBOR
发表于 2025-3-22 11:15:47
https://doi.org/10.1007/978-3-642-73728-2ter interest in areas other than computer science, such as physics and engineering. This chapter provides a brief overview of the recent developments in artificial intelligence. Furthermore, several ideas of different approaches using deep learning in computational mechanics are introduced. When tra
壁画
发表于 2025-3-22 15:00:40
Desorption Processes in Planetary Science this using data. This chapter gives an overview of the fundamental concepts, including the data structures, learning types, and the different machine learning tasks. Additionally, the gradient descent method is introduced to illustrate how many machine learning algorithms learn through experience.
壁画
发表于 2025-3-22 20:44:24
R. L. Kurtz,R. Stockbauer,T. E. Madeys. ANNs serve as universal function approximators, meaning that a sufficiently complex neural network can learn almost any function in any dimension. This flexibility, combined with backpropagation and a learning algorithm, enables to learn unknown functions with an astonishing accuracy. This chapte
Indecisive
发表于 2025-3-22 21:25:09
C. Z. Dong,P. Nordlander,T. E. Madeyhysics and engineering have also taken advantage of machine learning by tuning these methods for their purpose. This chapter starts with a general review and then describes combined models and surrogate models. The idea is to show how machine learning can be used in physics and engineering without d
变化
发表于 2025-3-23 01:40:19
D. Feldmann,J. Kutzner,K. H. Welgel equation, including the residual in the cost function. PINNs can be used for both solving and discovering differential equations. In this chapter, PINNs are illustrated with three one-dimensional examples. The first example shows how the displacement of a static bar can be computed. The temperatur
fulmination
发表于 2025-3-23 06:07:38
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