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Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design
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Machine Learning for Model Order Reduction (Hardcover, 1st ed. 2018)
Loot Price: R3,365
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Machine Learning for Model Order Reduction (Hardcover, 1st ed. 2018)
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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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