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An initial ensemble of fluid flow simulation models is created where the full range of uncertain parameters are acknowledged using experimental design methods, and an evolutionary algorithm is used for optimization in the history matching process.
In this framework, the architecture of the hybrid soft computing models is created and evolved by using the modified probabilistic incremental program evolution (MPIPE) algorithm, and the parameters used in hybrid soft computing models can be optimized using a class of optimization techniques.
Then, a hierarchical model, which consists of the region models and pixel models, is created.
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Finite element models are created with ANSYS.
8 30 models were created with LearnModel using default parameters.
The models are created using the finite element package ABAQUSTM.
In this paper models are created to explain this behavior.
Distinct models were created for native versus decellularized liver.
The simulation models are created in MATLAB - SIMULINK.
Multinomial logistic regression models were created and compared using Akaike Information Criterion.
Two working models were created of the turbine system.
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