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Significant advances in computing capabilities, decreasing storage costs, and the rising costs associated with physical experiments have contributed to an increase in the use of numerical modeling and simulation. The inclusion of computer simulations in the study and design of complex engineering systems has introduced many new challenges. For example, code verification must be used to confirm that the underlying equations are being solved correctly. In addition, validation processes should be applied to answer questions of correctness of the equations and models for the physics being modeled and the application being studied. Moreover, validation metrics must be carefully chosen in order to explicitly compare experimental and computational results and quantify the uncertainties in these comparisons. Finally, codes must be calibrated using real data. In this talk, we will give an overview of the validation process for an electrical circuit simulator, Xyce. We will focus on the problem of simulator calibration for some electrical device models. In this work, calibration is posed as the optimization problem of minimizing the differences between the simulated data and some experimental data. We will describe the process of experimental data collection, the relevant metrics, the selection of the simulator parameters being calibrated, and the objective function. We will introduce a novel optimization approach which hybridizes direct search and stochastic process for the solution of this problem.