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Dealing with Model Errors in Approximation Model-Based Optimization
Approximation models are often used in place of complex analysis code during optimization. Uncertainties in the model parameters could lead to inaccuracies in the process. This paper presents a Bayesian approach for finding an optimum that is robust to the uncertainty in model parameters. We test the proposed methodology with two model types applied to standard electromagnetics benchmark problems
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