Novel tool to select surrogate modeling technique for design space approximation

Overview Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate. We have constructed a tool to recommend the appropriate surrogate modelling technique for a given dataset using attributes calculated from the input and output values. The tool identifies the appropriate surrogate modeling techniques with an accuracy of 98%, a precision of 93%, and a correlation coefficient of 0.95 between the predicted and the actual appropriate techniques.