Sobolsensitivitetsanalyse

Sobol sensitivity analysis is a method used to determine how the variance of the output of a mathematical model can be attributed to different sources of variation in its input. It is a global method, meaning it considers the entire range of input values and their interactions, rather than focusing on local changes around a specific point. Developed by Ivan Sobol, this technique decomposes the total variance of the model's output into fractions that correspond to individual input factors and their interactions with other factors.

The core idea is to quantify the importance of each input variable. The first-order Sobol index measures the contribution of a single input variable to the output variance, assuming all other variables are fixed. The total-order Sobol index measures the contribution of a single input variable to the output variance, considering all possible interactions with other input variables. By calculating these indices, researchers can identify which input parameters have the most significant impact on the model's behavior. This is crucial for model simplification, parameter fixing, and identifying areas where more research or data collection might be beneficial.

Sobol sensitivity analysis is widely applied in various fields, including engineering, environmental science, economics, and climate modeling, where complex systems with numerous uncertain parameters are common. It provides a robust way to understand the sensitivity of model outputs to uncertainties in input data.