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Title
Machine learning for model order reduction / Khaled Salah Mohamed.
ISBN
9783319757148 (electronic book)
3319757148 (electronic book)
9783319757131
Published
Cham, Switzerland : Springer, 2018.
Language
English
Description
1 online resource (xi, 93 pages) : illustrations
Item Number
10.1007/978-3-319-75714-8 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
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.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 7, 2018).
Available in Other Form
Print version: 9783319757131
Chapter1: Introduction
Chapter2: Bio-Inspired Machine Learning Algorithm: Genetic Algorithm
Chapter3: Thermo-Inspired Machine Learning Algorithm: Simulated Annealing
Chapter4: Nature-Inspired Machine Learning Algorithm: Particle Swarm Optimization, Artificial Bee Colony
Chapter5: Control-Inspired Machine Learning Algorithm: Fuzzy Logic Optimization
Chapter6: Brain-Inspired Machine Learning Algorithm: Neural Network Optimization
Chapter7: Comparisons, Hybrid Solutions, Hardware architectures and New Directions
Chapter8: Conclusions.
