Computational Mechanics with Neural Networks

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.

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Computational Mechanics with Neural Networks

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.

126.99 In Stock
Computational Mechanics with Neural Networks

Computational Mechanics with Neural Networks

Computational Mechanics with Neural Networks

Computational Mechanics with Neural Networks

eBook1st ed. 2021 (1st ed. 2021)

$126.99  $169.00 Save 25% Current price is $126.99, Original price is $169. You Save 25%.

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Overview

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.


Product Details

ISBN-13: 9783030661113
Publisher: Springer-Verlag New York, LLC
Publication date: 02/26/2021
Series: Lecture Notes on Numerical Methods in Engineering and Sciences
Sold by: Barnes & Noble
Format: eBook
File size: 41 MB
Note: This product may take a few minutes to download.

Table of Contents

Part I: Machine Learning Technologies for Computational Mechanics.-Chapter 1. Computers and Network.- Chapter 2. Feedforward Neural Networks.- Chapter 3. Deep Learning.- Chapter 4. Mutually Connected Neural Networks.- Chapter 5. Other Neural Networks.-Chapter 6. Other Algorithms and Systems.- Part II Applications: Chapter 7. Introductory Remarks. Chapter 8. Constitutive Models.- Chapter 9. Numerical Quadrature.- Chapter 10. Identifications of Analysis Parameters.
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