Sensitivity Analysis

Sensitivity analysis assesses how variations in input parameters affect the output of a system or model, aiming to identify the most influential factors and quantify their impact. Current research emphasizes efficient methods for high-dimensional problems, often employing variance-based measures (like Sobol indices), derivative-based techniques, and advanced metamodels (e.g., Gaussian processes, Polynomial Chaos Expansion) to handle computationally expensive simulations. These advancements are crucial for improving the reliability and interpretability of models across diverse fields, from engineering design and risk assessment to machine learning and healthcare, by enabling more robust predictions and informed decision-making. Furthermore, research is actively addressing challenges posed by correlated inputs and unmeasured confounders, leading to more accurate and reliable sensitivity analyses.