Structure‐exploiting polynomial chaos for uncertain optimal control problems with sparse derivative generation

Structure‐exploiting polynomial chaos for uncertain optimal control problems with sparse... We present advances in structure‐exploiting non‐intrusive polynomial chaos methods for the numerical solution of nonlinear optimal control problems with parametric uncertainties. In particular, we make use of the special structure induced by the spectral projection to reuse model derivatives and exploit sparsity information leading to a fast automatic sensitivity generation. This greatly reduces the computational effort of Newton‐type methods for the solution of the resulting high‐dimensional surrogate problem. The results are complemented by a complexity comparison and a numerical case study. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings in Applied Mathematics & Mechanics Wiley

Structure‐exploiting polynomial chaos for uncertain optimal control problems with sparse derivative generation

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2017 Wiley Subscription Services
ISSN
1617-7061
eISSN
1617-7061
D.O.I.
10.1002/pamm.201710327
Publisher site
See Article on Publisher Site

Abstract

We present advances in structure‐exploiting non‐intrusive polynomial chaos methods for the numerical solution of nonlinear optimal control problems with parametric uncertainties. In particular, we make use of the special structure induced by the spectral projection to reuse model derivatives and exploit sparsity information leading to a fast automatic sensitivity generation. This greatly reduces the computational effort of Newton‐type methods for the solution of the resulting high‐dimensional surrogate problem. The results are complemented by a complexity comparison and a numerical case study. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Journal

Proceedings in Applied Mathematics & MechanicsWiley

Published: Jan 1, 2017

References

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