Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Stochastic spectral methods for efficient Bayesian solution of inverse problems
Journal of Computational Physics
Journal of Computational Physics
Journal of Computational Physics
Reliability-based design optimization using kriging surrogates and subset simulation
Structural and Multidisciplinary Optimization
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
Numerical Analysis for Statisticians
Numerical Analysis for Statisticians
On the use of a class of interior point algorithms in stochastic structural optimization
Computers and Structures
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The problem of updating the parameters of a probabilistic model, describing spatially large structures, based on uncertain output information is analyzed. An unscented Kalman filter (UKF) variant is successfully used, although the analysis has not been cast in a filtering format. The performance of the UKF-variant is compared with other generic gradient-free inverse solvers. To reduce the computational demand of the stochastic model, sensitivity analysis for functional inputs and probabilistic homogenization techniques are used. Without loss of generality for this type of problems, the whole process is described along a specific application concerning diffusion phenomena and steel damage in RC.