Sentence examples for modeling methodologies including from inspiring English sources

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In this article, we summarize the advantages and limitations of various modeling methodologies, including both stoichiometric and kinetic approaches, using a simple model system to illustrate their application.

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Based on a calibrated, nonlinear, 2D numerical modeling methodology including backfill abutment interaction, soil pile interaction and critical construction joints, a parametric study of 243 analysis cases was performed, consistent with the AASHTO 75-year bridge life.

Commonly employed modeling methodologies using indicators generated from real world data include logistic regression models, artificial neural networks, and other classification algorithms.

Additionally, uncertainty of costs and demand parameters can be taken into account in the model, and new solution methodologies including uncertainty can be developed via fuzzy models.

They will be sharing data and research methodologies, including numerical modeling.

There are mainly three types of methodologies, including the game theory model, simulation model and time series model [8].

An important research direction to pursue involves developing comprehensive methodologies, including theoretical models, empirical studies and real-world case studies.

The conference is receptive to a wide range of research perspectives and methodologies, including formal theoretical modeling, quantitative research drawing upon historical/archival data, surveys, or publicly available data, and qualitative research.

PhylOnt is an extensible ontology with concepts describing tree types and tree building methodologies including estimation methods, models and programs.

According to Goldstein, entrepreneurs who enter the program embark on a series of rigorous workshops, employing various methodologies including customer development, design for manufacturing, business modeling and human-centered design.

These modelling methodologies thus complement the process knowledge included in white-box models with predictions based on data in areas where the white-box model assumptions are not valid or where white-box models do not provide accurate predictions.

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