Accelerated Adaptive Surrogate-Based Optimization Through Reduced-Order Modeling

Abstract : The efficient global optimization approach was often used to reduce the computational cost in the optimization of complex engineering systems. This algorithm can, however, remain expensive for large-scale problems because each simulation uses the full numerical model. A novel optimization approach for such problems is proposed in this paper, in which the numerical model solves partial differential equations involving the resolution of a large system of equations, such as by finite element. This method is based on the combination of the efficient global optimization approach and reduced-basis modeling. The novel idea is to use inexpensive, sufficiently accurate reduced-basis solutions to significantly reduce the number of full system resolutions. Two applications of the proposed surrogate-based optimization approach are presented: an application to the problem of stiffness maximization of laminated plates and an application to the problem of identification of orthotropic elastic constants from full-field displacement measurements based on a tensile test on a plate with a hole. Compared with the crude efficient global optimization algorithm, a significant reduction in computational cost was achieved using the proposed efficient reduced-basis global optimization.
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Contributor : Pierre Naegelen <>
Submitted on : Monday, December 3, 2018 - 3:38:15 PM
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Moindze Soilahoudine, Christian Gogu, Christian Bes. Accelerated Adaptive Surrogate-Based Optimization Through Reduced-Order Modeling. AIAA Journal, American Institute of Aeronautics and Astronautics, 2017, 55 (5), pp.1681--1694. ⟨10.2514/1.J055252⟩. ⟨hal-01943030⟩



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