Sentence examples for computational framework to model from inspiring English sources
Therefore, we use a computational framework to model the hybrid masonry that uses a typical plasticity model with hardening for the steel components and a nonlocal two-scalar damage model that accounts for tension and compression for the masonry panel.
In this work, we first propose an original and efficient computational framework to model continuous diffusion MRI (dMRI) signals and analytically recover important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF).
To do this, we have developed an open computational framework to compare, quantitatively, the results from theoretical models of retinotopic map formation against experimental data.
We have developed a computational framework to harness phenotypic information from model organisms and single-gene human hereditary disorders to gain insights into the genetic etiology of the complex phenotypes of CNV disorders (Fig. 1).
We describe here the development of a computational framework to comprehensively search phenotypic information from model organisms and single-gene human hereditary disorders, and thus speed the interpretation of the complex phenotypes of CNV disorders.
We applied the computational framework to elucidate the miRNA-mediated regulatory network in the Mir122a -/- mouse model, which has an altered transcriptome and progressive liver disease.
During the past decades, manifold inference and learning methods have been proposed to integrate raw data to computational frameworks for network models, such as (probabilistic) Boolean network and (dynamic) Bayesian network, systematic differential/difference equations [ 1- 6], information theory-based modelling [ 7- 10], graph and control theoretic approaches [ 11- 13].
Acceptance of the definitions proposed brings us to the comparison of two computational frameworks for modelling real-life disease evolution for in- and outpatients with major unipolar depression over a period of 10 years.
In particular, the integration of highly detailed empirical data in computational frameworks designed to model the spread of infectious diseases raises the issue of assessing which representations of the raw data work best to inform the models.
The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts.
