Sentence examples for minimization of experimental from inspiring English sources
Minimizing the level changes implies the minimization of experimental cost.
Here we review recent advances from this field by scrutinizing commonly applied diversification methods and highlighting crucial strategies for the minimization of experimental effort.
Energy minimization of a correct experimental crystal structure should lead to relatively smaller distortions compared to minimization of an incorrect structure, thereby providing possibilities for quantitative structure validation (Van de Streek & Neumann, 2010 ▶).
Regression was performed to obtain the parameters that best fit the model to the experimental data through minimization of the difference between experimental points and model predictions.
The constitutive equations were obtained by least squares minimization of deviations between the experimental variations of H and s and ones predicted by a simplified engineering model.
Therefore, we will present an assessment of a variety of current density functionals, in which the dispersion-correction parameters were fitted with respect to a minimization of the deviation between experimental and computed crystal structures.
The constants of these equations were obtained by least squares minimization of deviations between the experimental variations of H and s and ones predicted by a simplified engineering model formulated for this purpose.
Optical tomography is mathematically treated as a non-linear inverse problem where the optical properties of the probed medium are recovered through the minimization of the errors between the experimental measurements and their predictions with a numerical model at the locations of the detectors.
Uncertainty minimization using experimental measurements can reduce the uncertainty space of the rate coefficients of elementary reactions, and further reduce the uncertainties of model predictions.
This hypothesis, which is hardly new and is well supported by experimental data, allows minimization of the number of fitting parameters.
The unknown parameters are estimated from experimental data by minimization of the unweighted least-squares function, using a non-linear optimization method (complex algorithm).
