Difference between revisions of "Surrogate modeling"
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Revision as of 17:31, 22 August 2013
Surrogate modeling refers to the process of creating a function from data that approximates the relation underlying the data. For example if you have a data set with two variables, Height and Weight, you could create a surrogate model Weight = f(Height) so that given you data for Height, you can generate corresponding values of Weight.
Surrogate modeling is used for:
- Enabling capabilities that depend on functional variables, such as optimization and continuous visualization
- Speeding up calculations: surrogate models are generally fast, relatively simple mathematical expressions that can be evaluated almost instantly.
- And more...
