Name
Abstract | ||
|---|---|---|
Dynamically data-driven application systems integrate computational simulations and physical measurements in a symbiotic feedback control system. Inverse problems in this framework use data from measurements along with a numerical model to estimate the parameters or state of a physical system of interest. Uncertainties in both the measurements and the computational model lead to inaccurate estimates. This work develops a methodology to estimate the impact of different errors on the variational solutions of inverse problems. The focus is on time evolving systems described by differential equations, and on a particular class of inverse problems, namely, data assimilation. The computational algorithm uses first order and second order adjoint models. In a deterministic setting the methodology provides a posteriori error estimates for the inverse solution. In a probabilistic setting it provides an a posteriori quantification of uncertainty in the inverse solution, given the uncertainties in the model and data. Numerical experiments with the shallow water equations in spherical coordinates illustrate the use of the proposed error estimation machinery in both deterministic and probabilistic settings. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1137/140990036 | SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION |
Keywords | Field | DocType |
dynamically data-driven application systems (DDDAS),inverse problems,sensitivity analysis,a posteriori error estimates | Differential equation,Mathematical optimization,First order,Physical system,A priori and a posteriori,Inverse problem,Probabilistic logic,Control system,Data assimilation,Mathematics | Journal |
Volume | Issue | ISSN |
3 | 1 | 2166-2525 |
Citations | PageRank | References |
9 | 0.82 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| vishwas rao | 1 | 9 | 0.82 |
| Adrian Sandu | 2 | 325 | 58.93 |