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Title: Multilevel-Multifidelity Acceleration of PDE-Constrained Optimization.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
Defense Advanced Research Projects Agency (DARPA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the AIAA SciTech 2017 held January 9-13, 2017 in Grapevine, Texas.
Country of Publication:
United States

Citation Formats

Monschke, Jason A., and Eldred, Michael S.. Multilevel-Multifidelity Acceleration of PDE-Constrained Optimization.. United States: N. p., 2016. Web. doi:10.2514/6.2017-0132.
Monschke, Jason A., & Eldred, Michael S.. Multilevel-Multifidelity Acceleration of PDE-Constrained Optimization.. United States. doi:10.2514/6.2017-0132.
Monschke, Jason A., and Eldred, Michael S.. 2016. "Multilevel-Multifidelity Acceleration of PDE-Constrained Optimization.". United States. doi:10.2514/6.2017-0132.
title = {Multilevel-Multifidelity Acceleration of PDE-Constrained Optimization.},
author = {Monschke, Jason A. and Eldred, Michael S.},
abstractNote = {Abstract not provided.},
doi = {10.2514/6.2017-0132},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month =

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  • Many engineering application problems use optimization algorithms in conjunction with numerical simulators to search for solutions. The formulation of relevant objective functions and constraints dictate possible optimization algorithms. Often, a gradient based approach is not possible since objective functions and constraints can be nonlinear, nonconvex, non-differentiable, or even discontinuous and the simulations involved can be computationally expensive. Moreover, computational efficiency and accuracy are desirable and also influence the choice of solution method. With the advent and increasing availability of massively parallel computers, computational speed has increased tremendously. Unfortunately, the numerical and model complexities of many problems still demand significant computationalmore » resources. Moreover, in optimization, these expenses can be a limiting factor since obtaining solutions often requires the completion of numerous computationally intensive simulations. Therefore, we propose a multifidelity optimization algorithm (MFO) designed to improve the computational efficiency of an optimization method for a wide range of applications. In developing the MFO algorithm, we take advantage of the interactions between multi fidelity models to develop a dynamic and computational time saving optimization algorithm. First, a direct search method is applied to the high fidelity model over a reduced design space. In conjunction with this search, a specialized oracle is employed to map the design space of this high fidelity model to that of a computationally cheaper low fidelity model using space mapping techniques. Then, in the low fidelity space, an optimum is obtained using gradient or non-gradient based optimization, and it is mapped back to the high fidelity space. In this paper, we describe the theory and implementation details of our MFO algorithm. We also demonstrate our MFO method on some example problems and on two applications: earth penetrators and groundwater remediation.« less
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