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The sub-populations that are used to evaluate the solution locally can obtain better performance compared to systems that only use one population for evaluating the solution.
We describe a high-order accurate method for evaluating the solution at arbitrary points inside the domain, including points close to the domain boundary.
Accordingly, different conceptual frameworks were proposed that describe the cognitive processes of problem solving such as understanding the problem, building adequate representations of the problem, developing hypotheses, conducting experiments, and evaluating the solution (Jonassen [2011]; OECD [2005]).
The overall procedure is fast and accurate, depending only on the spatial discretization and the time step selected for evaluating the solution without the need of any integration step.
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The model residual time series (i.e., the difference between the summed measured mass spectrum and its modeled approximation; Fig. 5g) is particularly useful for evaluating the solutions of PMF and related methods [ 38, 41].
The reason is that we have to evaluate the solution at past timesteps in order to get an evaluation of the right-hand side function.
Unlike other solution-based adaptive gridding schemes, wherein the overhead of recomputing the grid and re-evaluating the solution on the adapted grid leads to higher computational costs compared to a nonadapted calculation, the present methodology reduces the computational time required to obtain the solution.
A usability test was conducted to evaluate the solution.
A simulation program is developed to evaluate the solution of each method.
However, as in Veale et al.'s work [42], no test was conducted to evaluate the solution.
We also develop bounding procedures to evaluate the solution quality when the optimal solution is not available.
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