Machine Learning for Model Order Reduction (Hardcover, 1st ed. 2018)

This Book discusses machine learning for model order reduction, which can be used in modern VLSI design to predict the behavior of an electronic circuit, via mathematical models that predict behavior. The author describes techniques to reduce significantly the time required for simulations involving large-scale ordinary differential equations, which sometimes take several days or even weeks. This method is called model order reduction (MOR), which reduces the complexity of the original large system and generates a reduced-order model (ROM) to represent the original one. Readers will gain in-depth knowledge of machine learning and model order reduction concepts, the tradeoffs involved with using various algorithms, and how to apply the techniques presented to circuit simulations and numerical analysis. Introduces machine learning algorithms at the architecture level and the algorithm levels of abstraction; Describes new, hybrid solutions for model order reduction; Presents machine learning algorithms in depth, but simply; Uses real, industrial applications to verify algorithms.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	March 2018
First published:	2018
Authors:	Khaled Salah Mohamed
Dimensions:	235 x 155 x 14mm (L x W x T)
Format:	Hardcover
Pages:	93
Edition:	1st ed. 2018
ISBN-13:	978-3-319-75713-1
Categories:	Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Circuits & components Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-319-75713-X
Barcode:	9783319757131

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