Abstract

Various uncertainties jeopardize predictability of complex systems arising in different application domains which stimulates the efforts to improve the accuracy of numerical forecast by integrating observations and simulations. This paper addresses a simulation driven approach to the design of feedback controlled observer for complex nonlinear systems where application of analytical techniques become cumbersome or impractical. The quality of state estimations of this approach is tested in applications to some time-dependent and stiff nonlinear systems under significant parameter and initial data uncertainty arising in atmospheric chemistry modeling. We demonstrate that this approach yields robust and rapid state estimation for these systems and simultaneously provides denoising if the corresponding measurements are corrupted by noise.

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