Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

Sách Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics (Sách keo gáy, bìa mềm)

The use of machine learning in mechanics is booming.

Algorithms inspired by developments in the field of artificial

intelligence today cover increasingly varied fields of application. This

book illustrates recent results on coupling machine learning with

computational mechanics, particularly for the construction of surrogate

models or reduced order models. The articles contained in this

compilation were presented at the EUROMECH Colloquium 597, «Reduced

Order Modeling in Mechanics of Materials», held in Bad Herrenalb,

Germany, from August 28th to August 31th 2018. In this book, Artificial

Neural Networks are coupled to physics-based models. The tensor format

of simulation data is exploited in surrogate models or for data pruning.

Various reduced order models are proposed via machine learning

strategies applied to simulation data. Since reduced order models have

specific approximation errors, error estimators are also proposed in

this book. The proposed numerical examples are very close to engineering

problems. The reader would find this book to be a useful reference in

identifying progress in machine learning and reduced order modeling for

computational mechanics.

Categories:Engineering

Year:2019

Language:english

Pages:256

https://zalo.me/0969436832

0969436832