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Machine Learning for Model Order Reduction

203,26 
203,26 
2025-07-31 203.2600 InStock
Nemokamas pristatymas į paštomatus per 13-17 darbo dienų užsakymams nuo 19,00 

Knygos aprašymas

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.

Informacija

Autorius: Khaled Salah Mohamed
Leidėjas: Springer Nature Switzerland
Išleidimo metai: 2019
Knygos puslapių skaičius: 108
ISBN-10: 3030093077
ISBN-13: 9783030093075
Formatas: Knyga minkštu viršeliu
Kalba: Anglų
Žanras: Electronics: circuits and components

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