| dc.description.abstract | Computational steering integrates modeling, computation, data analysis, visualization, and data input components of a simulation. Since the simulation space is, in general, very large and continuous, selecting discrete simulation points that can reasonably represent the whole simulation space is difficult. We need to interpolate the missing values and cover a continuous region of interest in the simulation space. We describe an approach that, in an iterative manner, allows a domain expert to interactively select data points (design of experiments), approximate the values in a continuous region of the simulation space (regression) and automatically find the best points in that continuous region based on the specified constraints and objectives (optimization), using the regression and aggregated data. Once the objectives are found, the data points in the neighborhood of the objective are generated by the simulation tool thus providing a denser coverage of the regions of interest. | en_US |
