Engineering systems are generally susceptible to parameter uncertainties that influence real-time system performance and long-term system reliability. However, designers and engineers must design system solutions that are both optimal and dependable. Robust design techniques and robust optimization methods in particular, have emerged as promising methodologies to address the problem of dealing with parameter uncertainties. This research advances a robust optimization approach that exploits gradient information embedded in proximate NURBs control point clusters that are inherent in NURBs metamodel design space representations. The proximate control point clusters embody the target sensitivity profile and therefore include robust optimal solutions, thus enabling selective optimization within regions associated with the clusters. This robust optimization framework has been implemented and is demonstrated on unconstrained robust optimization problems from two test functions and a constrained robust optimization problem from a practical engineering design problem.

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