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The paper presents the model, the computational process and applications.
In fully 3D CFD-based model, the computational requirements increase by several orders of magnitude.
By reducing the number of equations involved in the numerical model, the computational efficiency is significantly increased.
The results were successfully compared with those obtained from a full CFD model, the computational time dropping from several weeks to a few seconds.
It is shown that by employing a neural network trained by a simplified physical model, the computational cost can be reduced close to an order of magnitude without significant loss of accuracy.
While in principle such algorithms could be used in-lieu of a linear equation of state in a groundwater model, the computational overhead is such that the use of a more simplified equation of state is preferred.
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The mathematical models, the computational scheme, and the results corresponding to different loading scenarios are discussed.
For spatially resolved models, the computational effort is too large to fit all models on the entire genome.
With increasing size and complexity of genome-scale models the computational effort for their analysis increased as well.
Modeling, particularly the computational modeling of statistical correlations, is said to provide an efficient tool for finding such hypotheses amidst the mass of data [ 7, 8].
Moreover, the new model preserves the computational simplicity of the cellular automata models.
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