Authors:
- 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
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (8 chapters)
-
Front Matter
-
Back Matter
About this book
- 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.
Authors and Affiliations
-
Mentor Graphics, Heliopolis, Egypt
Khaled Salah Mohamed
About the author
Bibliographic Information
Book Title: Machine Learning for Model Order Reduction
Authors: Khaled Salah Mohamed
DOI: https://doi.org/10.1007/978-3-319-75714-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-75713-1Published: 09 March 2018
Softcover ISBN: 978-3-030-09307-5Published: 04 January 2019
eBook ISBN: 978-3-319-75714-8Published: 02 March 2018
Edition Number: 1
Number of Pages: XI, 93
Topics: Circuits and Systems, Processor Architectures, Electronics and Microelectronics, Instrumentation