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Michael Silverstein, a professor of anthropology, linguistics and psychology at the University of Chicago, gave me an iterative history of space, time and gender as topics covered by researchers in this field.
Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step.
This article describes a 10-year iterative history of using the Theory of Silencing the Self (TSTS) as an explanatory model that was found to be gender sensitive, culturally relevant, and helpful in guiding a community-based participatory research group of low-income, HIV-infected Black women living in Boston, Massachusetts.
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Therefore a brief history on iterative methods is reviewed in the following.
This paper describes a new iterative procedure for probabilistic history matching using a discrete Latin Hypercube (DLHC) sampling method and nonparametric density estimation.
Two iterative methods of developing time histories compatible with multi-damping spectra are presented.
History matching is an iterative process that modifies a reservoir model to reproduce field behavior.
This paper introduces a new method for history matching, combining the Iterative Discrete Latin Hypercube (IDLHC) with multi-start Simulated Annealing methods.
As shown in Fig. 1, automatic history matching is an iterative procedure where the unknown reservoir parameters are adjusted automatically with an optimizer to match the observed production or pressure data.
The proposed approach is based on predicting average reservoir pressure at different time intervals from stabilized surface production-history data using an iterative mode of calculations.
Our method is motivated by the iterative VanCittert algorithm, which has a long history as a simple and efficient approach for image restoration.
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