Hybrid Data Science (HDS) Modeling Approaches for Industrial and Scientific Applications - Tapa blanda

Madasu, Srinath; Rangarajan, Keshava Prasad; Marotta, Egidio

 
9798834831501: Hybrid Data Science (HDS) Modeling Approaches for Industrial and Scientific Applications
Tapa blanda
ISBN 13: 9798834831501
Editorial: Independently published, 2022

Sinopsis

This book details the basics of Deep Learning, Machine Learning and numerical grid based methods such as finite element, finite volume and finite difference. Hybrid models combining grid based methods and machine learning in particular neural networks in covered in detail. It will take you through a step by step approach making the user to understand both the physics domain and data science to hybridize both. The book describes the methods to create hybrid data science models for industry and scientific applications. The book covers some practical applications where the hybrid techniques can be used. It also provides sample python codes for the several chapters discussed in the book. This book represents our attempt to make hybrid physics and deep learning approachable, meaningful and present the basic concepts, context, and the code. We believe that this might be the first book published using such hybrid physics/machine learning technology.

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Editorial
Independently published
Año de publicación
2022
Idioma
Inglés
ISBN 13 
9798834831501
Encuadernación
Tapa blanda
Número de páginas
827
Contacto del fabricante
Manufactured by Amazon on behalf of the author
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