Skip to main content

Stochastic model-based source identification

Publication ,  Conference
Calkins, L; Khodayi-Mehr, R; Aquino, W; Zavlanos, M
Published in: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
June 28, 2017

In this paper we investigate the use of Stochastic Reduced Order Models (SROMs) for solving Stochastic Source Identification (SSI) problems in steady-state transport phenomena given statistics of the system state at a small number of locations. We capture the physics of the transport phenomenon by a Partial Differential Equation (PDE) which we discretize using the finite element method. The SSI problem is then formulated as a stochastic optimization problem constrained by the PDE, and then transformed into a deterministic one after representing the random quantities with a low-dimensional discrete SROM. The small number of samples given by SROMs requires only a small number of PDE solves at each optimization iteration in order to obtain a solution to the SSI problem, defined as a distribution of possible source locations and intensities. We provide simulations to demonstrate the effectiveness of SROMs in capturing uncertainty. We also demonstrate the ability of SROMs to capture multiple independent sources of uncertainty, in particular, we consider uncertainty in the location of the measurements which has practical implications in robotics applications.

Duke Scholars

Published In

2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

DOI

Publication Date

June 28, 2017

Volume

2018-January

Start / End Page

1272 / 1277
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Calkins, L., Khodayi-Mehr, R., Aquino, W., & Zavlanos, M. (2017). Stochastic model-based source identification. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (Vol. 2018-January, pp. 1272–1277). https://doi.org/10.1109/CDC.2017.8263831
Calkins, L., R. Khodayi-Mehr, W. Aquino, and M. Zavlanos. “Stochastic model-based source identification.” In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, 2018-January:1272–77, 2017. https://doi.org/10.1109/CDC.2017.8263831.
Calkins L, Khodayi-Mehr R, Aquino W, Zavlanos M. Stochastic model-based source identification. In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. 2017. p. 1272–7.
Calkins, L., et al. “Stochastic model-based source identification.” 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, vol. 2018-January, 2017, pp. 1272–77. Scopus, doi:10.1109/CDC.2017.8263831.
Calkins L, Khodayi-Mehr R, Aquino W, Zavlanos M. Stochastic model-based source identification. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. 2017. p. 1272–1277.

Published In

2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

DOI

Publication Date

June 28, 2017

Volume

2018-January

Start / End Page

1272 / 1277