In this Paper an Innovative Multistage Metamodeling Technique is Proposed for Linking Datacoming from Two Different Sources: Simulations and Experiments. the Model is Hierarchical, in Thesense that One Set of Data (the Experiments) is Considered to be more Reliable and it is Labeled as“high-Resolution” and the other Set (the Simulations) is Labeled as “low-Resolution”. the Results Ofexperiments is Obviously Fully Accurate, Except for the only Approximation due to the Measurementsystem and Given the Intrinsically Aleatory Nature of all Real Experiments. in the Proposed Approach,Gaussian Models are Used to Describe Results of Computer Experiments because they are Flexible Andthey can Easily Interpolate Data Coming from Deterministic Simulations. A Second Stage Model is Used,in Order to Link the Prediction of the First Model to the Real Experimental Data. for the Linkage Model,as in the First Stage, a Gaussian Process is Used. in this Second Stage a Random Parameter can be Addedto the Model, Known as Nugget, in Order to take into Account the Process Variability. this Kind Ofmetamodeling can have Different Purposes: Adjusting or Tuning the Simulations, Having a Better Tool Todrive the Design Process, Making an Optimization of a Parameter of Interest. in the Paper, its use Foroptimization of a Single Responsey with Two Design Variables x1 and x2 is Demonstrated. the Approachis Applied for Modeling the Crash Behavior in Three Point Bending of Metal Foam Filled Tubes.