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Model Induction from Data - Towards the next generation of computational engines in hydraulics and hydrology (Hardcover)
Loot Price: R3,457
Discovery Miles 34 570
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Model Induction from Data - Towards the next generation of computational engines in hydraulics and hydrology (Hardcover)
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There has been an explosive growth of methods in recent years for
learning (or estimating dependency) from data, where data refers to
known samples that are combinations of inputs and corresponding
outputs of a given physical system. The main subject addressed in
this thesis is model induction from data for the simulation of
hydrodynamic processes in the aquatic environment. Firstly, some
currently popular artificial neural network architectures are
introduced, and it is then argued that these devices can be
regarded as domain knowledge incapsulators by applying the method
to the generation of wave equations from hydraulic data and showing
how the equations of numerical-hydraulic models can, in their turn,
be recaptured using artificial neural networks. The book also
demonstrates how artificial neural networks can be used to generate
numerical operators on non-structured grids for the simulation of
hydrodynamic processes in two-dimensional flow systems and a
methodology has been derived for developing generic hydrodynamic
models using artificial neural network. The book also highlights
one other model induction technique, namely that of support vector
machine, as an emerging new method with a potential to provide more
robust models.
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