Adaptive Dimensionality Reduction for Fast Sequential Optimization With Gaussian Processes Academic Article uri icon


  • Available computational models for many engineering design applications are both expensive and and of a black-box nature. This renders traditional optimization techniques difficult to apply, including gradient-based optimization and expensive heuristic approaches. For such situations, Bayesian global optimization approaches, that both explore and exploit a true function while building a metamodel of it, are applied. These methods often rely on a set of alternative candidate designs over which a querying policy is designed to search. For even modestly high-dimensional problems, such an alternative set approach can be computationally intractable, due to the reliance on excessive exploration of the design space. To overcome this, we have developed a framework for the optimization of expensive black-box models, which is based on active subspace exploitation and a two-step knowledge gradient policy. We demonstrate our approach on three benchmark problems and a practical aerostructural wing design problem, where our method performs well against traditional direct application of Bayesian global optimization techniques.

published proceedings


author list (cited authors)

  • Ghoreishi, S. F., Friedman, S., & Allaire, D. L.

citation count

  • 20

complete list of authors

  • Ghoreishi, Seyede Fatemeh||Friedman, Samuel||Allaire, Douglas L

publication date

  • January 1, 2019 11:11 AM