parameterscan

Parameterscan is a method used in computational modeling and simulation to systematically explore the behavior of a model by varying input parameters across a defined range. This process is crucial for understanding how different inputs affect the model's outputs and for identifying optimal parameter values.

The core idea behind parameterscan is to create a grid or a set of specific points within the parameter space. For each point on this grid, the simulation is run with the corresponding parameter values, and the resulting output is recorded. This generates a dataset that maps parameter combinations to model outcomes.

By analyzing the results of a parameterscan, researchers can gain insights into the sensitivity of the model to individual parameters. They can identify which parameters have the most significant impact on the output and understand the relationships between different parameters. This can help in model calibration, validation, and the identification of emergent properties.

Parameterscan can be computationally intensive, especially when dealing with models that have many parameters or when exploring a wide range of values. Various techniques, such as Latin Hypercube sampling or Sobol sequences, can be employed to make the scan more efficient by selecting a representative subset of the parameter space. The visualization of parameterscan results, often through heatmaps or contour plots, is essential for interpreting the complex relationships observed.