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Model Induction from Data - Towards the next generation of computational engines in hydraulics and hydrology (Paperback)
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Model Induction from Data - Towards the next generation of computational engines in hydraulics and hydrology (Paperback)
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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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