- tuning1Tuning parameter to control exploration vs exploitation.
Default:1
C++ Type:double
Unit:(no unit assumed)
Range:tuning > 0
Controllable:No
Description:Tuning parameter to control exploration vs exploitation.
UpperConfidenceBound
Upper Confidence Bound acquisition function.
Overview
The UpperConfidenceBound acquisition function for parallel active learning (Bayesian optimization) is given by:
(1)
where, is a tuning parameter to boost exploration or exploitation, is the Gaussian process mean prediction, and is the Gaussian process standard deviation.
Input Parameters
- control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Controllable:No
Description:Adds user-defined labels for accessing object parameters via control logic.
- enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Controllable:No
Description:Set the enabled status of the MooseObject.