Sentence examples for capabilities of computational from inspiring English sources

Logical models provide new opportunities to improve the predictive capabilities of computational models as they are easy to construct, and analyses via computer simulations are efficient and capable of covering a relatively large number conditions, and have also been applied to drug target prediction [ 12],[ 37], as well as prediction of potential drug side-effects and sensitivity [ 38].

Phenomena are expected to be part of the modeling capabilities of computational tools.

Capabilities of computational fluid dynamics and other packages available for generating complex meshes can also be harnessed.

To investigate the limits and capabilities of computational tools, we conducted a study of the ability of the program Rosetta to predict sequences that recreate the authentic fold of thioredoxin.

Well established experimental approaches are available to offer a limited degree of characterization of mechanical properties within proteins, but the analytical capabilities of computational methods are evolving rapidly in their ability to accurately define the subtle and concerted structural dynamics that comprise allostery.

The purpose of this review paper is to identify current capabilities of Computational Fluid Dynamics (CFD) modelling techniques and areas where further scientific research is required, in order to identify how best CFD can be utilised in the future as a comprehensive modelling tool that enables naturally ventilated (NV) livestock buildings to be designed to reduce ammonia emissions.

A study was conducted to investigate the predictive capability of computational aeroacoustics with respect to a shrouded, subsonic, radial fan.

The predictive capability of computational fluid dynamics (CFD) fire models is highly dependent on the accuracy with which the source term due to fuel pyrolysis can be determined.

The other is the MD-pooling metric, and it allows for pooling the evidence from all relevant data of multi-response over the intended validation domain into a scalar measure to assess the global predictive capability of computational models.

This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques.

While first-principle kinetic modeling emerges as a powerful tool for catalyst selection, it has mainly been limited to using a single catalyst descriptor, simplified chemical kinetic models, and assumptions that question the predictive capability of computational results in the absence of addressing the effect of error in kinetic parameters.