On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Reliability-based design sensitivity by efficient simulation
Computers and Structures
Design and sensitivity analysis of dynamical systems subjected to stochastic loading
Computers and Structures
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The problem of updating response gradients with respect to chosen system parameters based on spatially sparse measurements is considered. The measurement noise and imperfections in mathematical modeling are treated as Gaussian white noise processes. The system states are augmented by response gradients with respect to system parameters and an extended set of equations in the state space is formulated. These equations are cast in the form of Ito's stochastic differential equations and measured data are assimilated into this model using Monte Carlo based Bayesian filtering tools. Illustrative examples include a few low dimensional dynamical systems with cubic and hereditary nonlinearities.