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We test this model through a free simulation experiment of collaborative process modelers structured around a set of modeling tasks.
Local interactions enter into the model through a graph topology and the corresponding graph-Laplacian matrix.
The main idea relies on specifying a parametric nonlinear model through a tree-growing procedure.
However, incorporating them in the reservoir model through a matching process is a challenging task.
The results of a complete experimental study are used to validate the model, through a comparison with the simulations result.
A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure.
We evaluate this model through a cross-sectional survey of process model users from a large European financial institution.
By learning the implicit vehicle model through a MSNN, our method outperforms a baseline blob detection method.
This design enabled rigorous validation of the concurrent binding model through a test of its predictive ability.
We tested the potential of our model through a case study carried out in Sorocaba, Sao Paulo State, Brazil.
The results of the proposed integrated approach are compared to those of the daily independent 2SSP model through a case study and real wind data.
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