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The oil reservoir, being treated as a multi-input, multi-output (MIMO) system in terms of injection inputs and production outputs, is identified within a robust surrogate modeling framework using a frequency-based system identification approach.
We illustrate the proposed modeling framework using lignite data from the Mavropigi mine in Northern Greece.
In this paper, LGC models, alone and combined with logistic regression models for the missing data mechanism, were fitted to the dataset in a structural equation modeling framework using Mplus software.
The computations were carried out in a second-moment turbulence modeling framework using a finite-volume technique.
In this study, we developed a modeling framework using a machine learning approach to predict hydrologic ratios for watersheds located in contiguous United States (CONUS) by utilizing a set of climate, soil, vegetation, and topographic variables.
In addition to item-level t tests, the correlations between teacher and student gains were tested within a multilevel modeling framework using HLM 7 (Raudenbush et al., 2011).
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The standard GTAP modeling framework uses a one-to-one relationship between industries and commodities.
The modeling framework uses a multilayer infrastructure network (MIN) concept, the computable general equilibrium (CGE) theory, and its spatial extension (SCGE), to formulate an equilibrium problem.
The general modeling framework used in our study is as follows.
This modeling framework uses a Markov chain Monte Carlo approach to generate samples from parameters' posterior distributions.
The Petri net modeling framework used in this work has been derived from the seminal work of Chaouiya et al. (2006).
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