Uncertainty Quantification
Uncertainty Quantification (UQ) is a mathematical and computational framework for characterizing, quantifying, and reducing uncertainties in models, simulations, and data-driven predictions. It involves analyzing how input uncertainties propagate through systems to affect outputs, enabling more reliable decision-making in fields like engineering, climate science, and finance. UQ methods include probabilistic modeling, sensitivity analysis, and Bayesian inference to assess confidence in predictions.
Developers should learn UQ when building models or simulations where accuracy and reliability are critical, such as in risk assessment, scientific computing, or machine learning applications. It is essential for quantifying prediction errors, optimizing designs under uncertainty, and ensuring robust performance in safety-critical systems like autonomous vehicles or medical diagnostics.