Timothy D. Robinson

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Surrogate-based-optimization methods provide a means to minimize expensive high-fidelity models at reduced computational cost. The methods are useful in problems for which two models of the same physical system exist: a high-fidelity model which is accurate and expensive, and a low-fidelity model which is less costly but less accurate. A number of model(More)
Surrogate-based-optimization methods provide a means to achieve high-fidelity design optimization at reduced computational cost by using a high-fidelity model in combination with lower-fidelity models that are less expensive to evaluate. This paper presents a provably convergent trust-region model-management methodology for variable-parameterization design(More)
Surrogate-based-optimization methods are increasingly used to minimize expensive high-delity models and therefore reduce the computational cost. The methods are useful in problems for which two models of the same physical system exist: a high-delity model which is accurate and expensive, and a low-delity model which is cheaper but less accurate. A number of(More)
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