Probability density adjoint for sensitivity analysis of the Mean of Chaos

Sensitivity analysis, especially adjoint based sensitivity analysis, is a powerful tool for engineering design which allows for the efficient computation of sensitivities with respect to many parameters. However, these methods break down when used to compute sensitivities of long-time averaged quant...

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Bibliographic Details
Main Authors: Blonigan, Patrick Joseph (Contributor), Wang, Qiqi (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor)
Format: Article
Language:English
Published: 2016-08-08T15:17:08Z.
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Online Access:Get fulltext
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100 1 0 |a Blonigan, Patrick Joseph  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics  |e contributor 
100 1 0 |a Blonigan, Patrick Joseph  |e contributor 
100 1 0 |a Wang, Qiqi  |e contributor 
700 1 0 |a Wang, Qiqi  |e author 
245 0 0 |a Probability density adjoint for sensitivity analysis of the Mean of Chaos 
260 |c 2016-08-08T15:17:08Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/103864 
520 |a Sensitivity analysis, especially adjoint based sensitivity analysis, is a powerful tool for engineering design which allows for the efficient computation of sensitivities with respect to many parameters. However, these methods break down when used to compute sensitivities of long-time averaged quantities in chaotic dynamical systems. This paper presents a new method for sensitivity analysis of ergodic chaotic dynamical systems, the density adjoint method. The method involves solving the governing equations for the system's invariant measure and its adjoint on the system's attractor manifold rather than in phase-space. This new approach is derived for and demonstrated on one-dimensional chaotic maps and the three-dimensional Lorenz system. It is found that the density adjoint computes very finely detailed adjoint distributions and accurate sensitivities, but suffers from large computational costs. 
546 |a en_US 
655 7 |a Article 
773 |t Journal of Computational Physics