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Originally, these expensive simulation samples would be obtained at random locations in the parameter space.
Various meta-modeling techniques have been developed to replace computationally expensive simulation models.
Learning experiments in the target task are realized over an accurate but computationally expensive simulation of the hose dynamics.
The use of surrogate models to approximate computationally expensive simulation models, e.g., large comprehensive finite element models, is widespread.
To address this shortcoming, an approach is developed that uses metamodels (surrogates of computationally expensive simulation models) to calculate robustness and other objectives.
In OLVFM-MOGA, the variable-fidelity metamodel (VFM) is constructed to replace the expensive simulation models to ease the computational burden.
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The goal of global surrogate modeling is to create an approximation model that mimics the original simulator, based on a limited number of expensive simulations, but can be evaluated much faster.
To reduce the computational expense, metamodels are commonly used to replace the actual expensive simulations or experiments.
There simply weren't enough supertaskers around, and the cost of finding them, bringing them to the lab, and running them through expensive simulations was prohibitive.
To improve the computation efficiency, surrogate models are used to replace expensive simulations of engineering problems.
The article focuses on "expensive" simulations, which have small sample sizes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com