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Urban modeling methods have traditionally followed a forward modeling approach.
However, structure-preserving methods have traditionally used spaces of piecewise polynomial basis functions for differential forms.
Such methods have traditionally been used to analyse the stability of rock blocks at free surfaces only.
These methods have traditionally been implemented using explicit Euler or explicit Runge-Kutta time marching schemes and consequently suffer from the Courant-Friedrichs-Lewy (CFL) time step restriction.
Landscape assessment methods have traditionally valued the landscape through a panel of experts with little or no participation of the population.
Unlike the vertical soundings, which aim to provide values of electrical resistivity with depth at a specific location, profiling methods have traditionally been designed to provide only a qualitative map of how the apparent resistivity varies over some regions.
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The predictive performance of models developed using these methods has traditionally been evaluated by assessing performance of the developed models against a portion of the data randomly selected for holdout.
Validation of gene regulation network (GRN) inference methods has traditionally been done using in silico networks.
Although research on data collection methods has traditionally referred to mode, given the complexity highlighted above where appropriate we use the term mode feature in this article.
However, the application of this training method has traditionally been applied to relatively narrow populations (e.g., football or track and field).
Although the phase-reduction method has traditionally been applied to deterministic limit cycle oscillators, there is growing interest in extending the method to take into account the effects of noise, in particular, the phenomenon of noise induced phase synchronization [8 15].
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