Sentence examples for surrogate response from inspiring English sources

The usual approach is to construct a surrogate response surface and then use it instead of the expensive model to carry out the uncertainty propagation task.

With the aid of the proposed surrogate response, failure probabilities of all the possible designs in a prescribed design space are calculated from a single run of SS.

This is mainly attributed to the strong correlation between the surrogate response and target response (e.g., factor of safety) of different designs concerned in geotechnical RBD.

To facilitate the integration of SS and expanded RBD, a generalized surrogate response f is proposed to define the driving variable, which is a key parameter in SS, for expanded RBD of geotechnical structures (e.g., soil retaining structures and foundations).

Recent retrospective European studies have demonstrated promising surrogate response rates for this kind of therapy (Rahbar et al., 2017; Brauer et al., 2017), and future randomized phase II/III trials are warranted to evaluate whether these will ultimately translate into survival benefits.

The freestream and jet controlling parameters are treated as input sources of variation, and a point-collocation non-intrusive polynomial chaos (NIPC) method is utilized to quantify the variations in the output surface heat flux and total surface heat load acting on the blunt body by identifying the maximum and minimum of surrogate response values predicted by the NIPC.

Equations are derived for integration of the surrogate response-aided SS and expanded RBD, and are illustrated using an embedded sheet pile wall design example and two drilled shaft design examples.

Results show that the proposed approach provides reasonable estimates of failure probabilities of different designs using a single run of the surrogate response-aided SS, and significantly improves the computational efficiency at small probabilities levels in comparison with direct MCS-based expanded RBD.

The surrogate response-aided SS is able to, simultaneously, approach the failure domains of all the possible designs in the design space by a single run of simulation and to generate more complete design information, which subsequently yields feasible designs with a wide range of combinations of design parameters.

Alternatively, further validation of QOL measurement based on surrogate responses would be welcomed.

On the other hand, the evaluation of quality of life using solely surrogate end points is incorrect by a methodological point of view and can lead to incorrect conclusions, that are just surrogate responses to surrogate questions.