Uncertainty Quantification

Uncertainty Quantification (UQ) is the systematic study of uncertainties in physical measurements, models, and simulations. It aims to identify, characterize, and propagate uncertainties arising from experimental errors, model assumptions, numerical approximations, and incomplete knowledge. In physics, uncertainty quantification is essential for validating theoretical predictions and comparing them with experimental data. UQ distinguishes between aleatoric uncertainty, which arises from inherent randomness, and epistemic uncertainty, which results from limited knowledge. Techniques include statistical analysis, sensitivity analysis, probabilistic modeling, and Bayesian inference. In computational physics, uncertainty quantification ensures that simulation results are robust and reliable. UQ plays a critical role in high-precision experiments, climate modeling, nuclear physics, and materials science. By rigorously assessing confidence levels and error bounds, uncertainty quantification enhances scientific credibility, reproducibility, and decision-making based on physical models.