It takes the average reader 1 hour and 34 minutes to read Machine Learning for Model Order Reduction by Khaled Salah Mohamed
Assuming a reading speed of 250 words per minute. Learn more
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.
Machine Learning for Model Order Reduction by Khaled Salah Mohamed is 93 pages long, and a total of 23,529 words.
This makes it 31% the length of the average book. It also has 29% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 2 hours and 8 minutes to read Machine Learning for Model Order Reduction aloud.
Machine Learning for Model Order Reduction is suitable for students ages 10 and up.
Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.
When deciding what to show young students always use your best judgement and consult a professional.
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