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Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. [1] [2] This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on the output.
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. [1] [2] Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.
Sensitivity analysis has important applications in model calibration. One application of sensitivity analysis addresses the question of "What's important to model or system development?" One can seek to identify important connections between observations, model inputs, and predictions or forecasts.
Sensitivity analysis identifies how uncertainties in input parameters affect important measures of building performance, such as cost, indoor thermal comfort, or CO 2 emissions. Input parameters for buildings fall into roughly three categories: Discrete design alternatives, e.g. different glazing options, number of storeys, etc.
A sensitivity analysis method widely used to screen factors in models of large dimensionality is the design proposed by Morris. [3] The Morris method deals efficiently with models containing hundreds of input factors without relying on strict assumptions about the model, such as for instance additivity or monotonicity of the model input-output ...
EE is applied to identify non-influential inputs for a computationally costly mathematical model or for a model with a large number of inputs, where the costs of estimating other sensitivity analysis measures such as the variance-based measures is not affordable. Like all screening, the EE method provides qualitative sensitivity analysis ...
One desired outcome is to summarize results in a concise and visually coherent form, using visualization tools such as tornado diagrams and sensitivity analysis graphs. At present, TEA is most commonly used to analyze technologies in the chemical, bioprocess, petroleum, energy, and similar industries. This article focuses on these areas of ...
Sensitivity analysis studies the relationship between the output of a model and its input variables or assumptions. Historically, the need for a role of sensitivity analysis in modelling, and many applications of sensitivity analysis have originated from environmental science and ecology .