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Skip to Search Results- 4Ensemble Kalman filter
- 2Inequality constraints
- 2Particle filters
- 1Constrained estimation
- 1Ensemble Kalman smoother
- 1Gaussian mixture model
- 2Huang, Biao (Chemical and Materials Engineering)
- 1Huang, Biao (Department of Chemical and Materials Engineering)
- 1Prasad, Vinay (Chemical and Materials Engineering)
- 1Prasad, Vinay (Department of Chemical and Materials Engineering)
- 1Prasad, Vinay(Chemical and Materials Engineering)
- 1Shah, Sirish (Chemical and Materials Engineering)
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Spring 2011
Balakrishnapillai Chitralekha, Saneej
The development of fast and efficient computer hardware technology has resulted in the rapid development of numerous computational software tools for making statistical inferences. The computational algorithms, which are the backbone of these tools, originate from distinct areas in science,...
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Fall 2018
The objective of this work is to study the problems that arise in state estimation for severely nonlinear systems. In practice, many processes are nonlinear, accompanied by uncertain parameters. The complexity of the model causes the probability density function (PDF) of the states to deviate...
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Spring 2013
Parameter estimation of a dynamic system is an important task in process systems engineering. The utilization of an augmented system offers the approach of estimating process states and parameters simultaneously. In practice, the parameters often satisfy certain constraints which should be...
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RESERVOIR HISTORY MATCHING USING CONSTRAINED ENSEMBLE KALMAN FILTER AND PARTICLE FILTER METHODS
DownloadSpring 2015
The high heterogeneity of petroleum reservoirs, represented by their spatially varying rock properties (porosity and permeability), greatly dictates the quantity of recoverable oil. In this work, the estimation of these rock properties, which is crucial for the future performance prediction of a...