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Furthermore, Stanford's ME Department has an excellent track record and international reputation for making contributions in both research and educational components of computational mathematics and parallel computing.
All three components of computational aerodynamics are actively developed: mathematical models of real aerodynamic processes, numerical algorithms, and high-performance computing.
The identification of the core components of computational thinking is also discussed by Chen et al. (2017).
For example, biological imaging and shape representation [1] [3], sequence analysis [4], regulatory genomics [5] and alternative splicing [6] are critical components of computational biology.
This anticipates a trend toward using server-side components of computational environments as a representation omnibus.
This realization is reflected in other quantitative genetics applications [ 11, 12] that derived various metrics to integrate these two components of computational efficiency.
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A tool for uncertainty propagation in a general, user-oriented, computational system, instead of a software library or a dedicated system, makes uncertainty propagation transparently computable also for vector/matrix measurands, even in the case of dynamic systems, and makes uncertainty evaluation an inherent component of computational processes instead of an optional, ad hoc, addendum to them.
Then, an engineered system can be decomposed into subsystems or components of which computational models are better understood in terms of PoF mechanisms or system performances of interest.
The main components of this computational tool consist of a hydrodynamic module, a hull surface modeling module, and an optimization module.
We argue that it is imperative that modelers select the right, and potentially differing levels of abstraction for different components of their computational models, as too global or too specific components will hinder scientific progress.
In the second part of the article, we describe an integrative neuro-computational account of action simulation, which links the neural substrate (as revealed in neuroimaging studies of action simulation) to the components of a computational architecture that includes internal modeling, action monitoring and inhibition mechanisms.
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