🏵️Sensitivity Analysis and Uncertainty Quantification plus AI Reasoning

Sensitivity analysis tackles the challenge of uncertain model inputs, examining how input variations affect output reliability. It differentiates between local and global methods, exploring input space to identify influential factors. Various model types, from linear to complex nonlinear systems, are analyzed. Input uncertainty, quantified through probability distributions, is explored using sampling methods like Latin Hypercube and Monte Carlo. Sensitivity metrics and indices, alongside regression and correlation, quantify factor influence. Techniques like perturbation, factorial analysis, and Bayesian methods enhance the analysis. The resulting inferences inform model robustness, guide data collection, and support decision-making across diverse fields, ultimately improving understanding and navigating uncertainty.