Fast data assimilation and optimal control methods and applications
Abstract/Contents
- Abstract
- With recent advances in environmental monitoring technology and remote sensing capabilities for data collection continually increasing, interest in data assimilation techniques for improving our understanding and predictive abilities related to natural systems has been mounting. One of the most popular data assimilation methods is Kalman filter (KF) which gives the minimum mean square error estimate for linear systems. Also, for nonlinear systems, the nonlinear KF variants can provide reasonably accurate estimation with the uncertainty of the estimation. However, application of KF for large-scale problems is computationally expensive. The computational cost of conventional Kalman filters scales quadratically with the number of unknowns, due to the cost of computing and storing covariance and Jacobian matrices, along with their matrix-vector products. In this work, we have developed new KF methods, the Spectral Kalman filter, and the Modified Compressed State Kalman Filter, that exploit the formulation of the state space models as well as the covariance structures to achieve computational efficiency while maintaining the quality of uncertainty quantification. Also, we have combined the SpecKF method with a fast optimization algorithm to simultaneously estimate the unknown parameters and state of the system and generate an optimal policy to control the system. This new fast optimization technique is called Rapid Feedback Control (RFC). The fast data assimilation and optimization algorithms developed here are for combined characterization, monitoring, and control of large-scale nonlinear systems. The proposed methods address the current lack of algorithms for reliable and cost-effective real-time estimation and optimization. The developed algorithms are tested and validated for three different hydrological applications. However, These methods can be extended to perform data assimilation for problems with similar characteristics in a wide range of applications.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Ghorbanidehno, Hojat |
|---|---|
| Associated with | Stanford University, Department of Mechanical Engineering. |
| Primary advisor | Darve, Eric |
| Thesis advisor | Darve, Eric |
| Thesis advisor | Kitanidis, P. K. (Peter K.) |
| Thesis advisor | Kokkinaki, Amalia |
| Advisor | Kitanidis, P. K. (Peter K.) |
| Advisor | Kokkinaki, Amalia |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Hojat Ghorbanidehno. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Hojat Ghorbanidehno
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