85 research outputs found
Dynamic mode decomposition using a Kalman filter for parameter estimation
A novel dynamic mode decomposition (DMD) method based on a Kalman filter is
proposed. This paper explains the fast algorithm of the proposed Kalman filter
DMD (KFDMD) in combination with truncated proper orthogonal decomposition for
many-degree-of-freedom problems. Numerical experiments reveal that KFDMD can
estimate eigenmodes more precisely compared with standard DMD or total
least-squares DMD (tlsDMD) methods for the severe noise condition if the nature
of the observation noise is known, though tlsDMD works better than KFDMD in the
low and medium noise level. Moreover, KFDMD can track the eigenmodes precisely
even when the system matrix varies with time similar to online DMD, and this
extension is naturally conducted owing to the characteristics of the Kalman
filter. In summary, the KFDMD is a promising tool with strong antinoise
characteristics for analyzing sequential datasets
Conjugate Simulation of Flow and Heat Conduction with a New Method for Faster Calculation
ABSTRACT
A novel method to predict current voltage characteristics of positive corona discharges based on a perturbation technique. I. Local analysis
A novel method to compute current-voltage characteristics (CVCs) of direct current positive corona discharges is formulated based on a perturbation technique. We use linearized fluid equations coupled with the linearized Poisson’s equation. Townsend relation is assumed to predict CVCs apart from the linearization point. We choose coaxial cylinders as a test problem, and we have successfully predicted parameters which can determine CVCs with arbitrary inner and outer radii. It is also confirmed that the proposed method essentially does not induce numerical instabilities
Adaptive Ensemble Kalman Filter Estimation of Nonlinear Structural Systems with Unknown Noise Covariance
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