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Machine Learning for Model Order Reduction

  • Khaled Salah┬áMohamed

Table of contents

  1. Front Matter
    Pages i-xi
  2. Khaled Salah Mohamed
    Pages 1-18
  3. Khaled Salah Mohamed
    Pages 89-89
  4. Back Matter
    Pages 91-93

About this book

Introduction

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.

Keywords

Model Order Reduction Techniques in VLSI Design Circuit simulation Machine learning for circuit simulation Genetic algorithms and circuit simulation Fuzzy logic and circuit simulation

Authors and affiliations

  • Khaled Salah┬áMohamed
    • 1
  1. 1.Mentor GraphicsHeliopolisEgypt

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-75714-8
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-75713-1
  • Online ISBN 978-3-319-75714-8
  • Buy this book on publisher's site