Evolutionary algorithms have been hybridized with metamodels (or surrogate models) in several works published in the last years, in order to substitute expensive computational fluid dynamics (CFD) simulations.
To address this issue, we explore several ways to improve the accuracy of surrogate models.
Using these thermal datasets, two-factor and multi-factor surrogate models are trained and several comparison simulations are conducted.
Low energy conformations derived from molecular dynamics simulations for 45 representatives of a combinatorial library of polyarylates were used in an improved modeling procedure (referred to as "3D surrogate model") instead of simplistic two-dimensional representations of polymer structures, which were used in several previous models (collectively referred to as "2D surrogate models").
The surrogate models obtained are used to analyze several steady-state plant-wide characteristics.
Several surrogate markers for CIA have been developed, which reflect different pathological aspects of the model, that is, markers for inflammation, bone degradation, and clinical wellbeing.
Although transgenic and gene knockout mouse models for several of these disorders exist, current efforts to find specific disease treatments and define precise pathogenesis mechanisms require better surrogate models with human cells.
Thus, how to choose sample points, how to build surrogate models, and how to evaluate the accuracy of surrogate models are key issues for surrogate modeling.
Many researchers recommended to use more complicated surrogate models.
To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models.
Compared to traditional surrogate models (polynomial response surfaces, kriging and radial basis functions), it has the advantage of giving light, compact forms with good predictive accuracy over a wide range of the design variables (several orders of magnitude).
