Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Virtual telemetry for dynamic data-driven application simulations
ICCS'03 Proceedings of the 2003 international conference on Computational science
Dynamically Identifying and Tracking Contaminants in Water Bodies
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
DDDAS Predictions for Water Spills
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
A dynamic data-driven approach for rail transport system simulation
Winter Simulation Conference
Mathematics and Computers in Simulation
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To improve the predictions in dynamic data driven simulations (DDDAS) for subsurface problems, we propose the permeability update based on observed measurements. Based on measurement errors and a priori information about the permeability field, such as covariance of permeability field and its values at the measurement locations, the permeability field is sampled. This sampling problem is highly nonlinear and Markov chain Monte Carlo (MCMC) method is used. We show that using the sampled realizations of the permeability field, the predictions can be significantly improved and the uncertainties can be assessed for this highly nonlinear problem.