Free shipping on orders over 19,990 Ft via Packeta, Fox Post Box, and DPD Collection Point

Check order status

Become part of a community of book lovers from all over the world and get access to a whole bunch of benefits. Create an account for free

Free delivery for purchases over 19 990 Ft

DPD courier 1 190 Ft Post 1 795 Ft Post 1 690 Ft Post 1 690 Ft GLS point 1 390 Ft FoxPost 1 190 Ft Packeta point 1 190 Ft DPD point 990 Ft GLS courier 1 790 Ft

Contact

How to shop

Help

My account

▸ Empty :-(

Free shipping on orders over 19,990 Ft via Packeta, Fox Post Box, and DPD Collection Point

Machine Learning for Model Order Reduction

Khaled Salah Mohamed

Language

English

Book Hardback

Libristo code: 18836073

Publishers Springer International Publishing AG, March 2018

This Book discusses machine learning for model order reduction, which can be used in modern VLSI des... Full description

Libristo code: 18836073

335 b

49 144 Ft

Low in stock at our supplier Shipping in 13-18 days

30-day return policy

Customers also purchased

Tratado Esotérico de Astrología Hermética V M Samael Aun Weor

Paperback

5 685 Ft

Haus am Hradschin Franz Spichtinger

Paperback

3 901 Ft

CARNET SELIMA LIGNES,96P,A5 PAPILLONSVINTAGE

Diary

3 841 Ft

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.