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A dataflow-based modeling framework for streaming applications integrates models for computational latency, cryptographic security levels, communication latency, and power consumption.
First, the theoretical formalism can be used to analyze additional applied models for computational complexity and optimality.
This study hypothesizes that reduced-scale models for computational fluid dynamics (CFD) simulations of wind flow and related processes can save numerical resources.
Tendon material properties are important for engineering applications such as the development of refined constitutive models for computational simulation or in the design of synthetic materials.
We propose a modeling paradigm, termed field inversion and machine learning (FIML), that seeks to comprehensively harness data from sources such as high-fidelity simulations and experiments to aid the creation of improved closure models for computational physics applications.
The high computational cost of solving numerically the fully compressible Navier Stokes equations, together with the poor performance of most numerical formulations for compressible flow in the low Mach number regime, has led to the necessity for more affordable numerical models for Computational Aeroacoustics.
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However, the most popular meta-models for computational computer models do not explicitly allow for categorical input variables.
In this paper, we propose a discrete model for computational color categorization and naming.
New turbulent model for computational mass transfer and its application to a commercial-scale distillation column.
Results from field measurement were used to develop a baseline model for computational experiment.
In the Tobit model, for computational convenience it is preferable to assume a censoring point at zero [45].
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