Uncertainty quantification (UQ) is increasingly critical for modelling complex systems in which input parameters or environmental conditions vary unpredictably. Polynomial chaos methods offer a ...

Uncertainty quantification (UQ) is a field of study that focuses on understanding, modeling, and reducing uncertainties in computational models and real-world systems. It is widely used in engineering ...

A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. Pollsters trying to predict presidential election results ...

Quantifying uncertainty in carbon accounting is essential at scales ranging from individual projects to country-level compensation for reducing emissions from deforestation and forest degradation.

Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial ...

A course in Uncertainty Quantification with an emphasis on formulating and computation to extract predictions and uncertainty in computational and simulation models (including when real data ...

Computational models and simulations have had an important role in engineering analysis since as far back as the 1960s. It is widely recognized that the use of modeling and simulation tools can make ...

Reliably quantifying and characterizing the quantum states of various systems is highly advantageous for both quantum physics research and the development of quantum technologies. Quantifying these ...