Uncertainty Quantification

Uncertainty quantification ... Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both ...

Uncertainty Quantification - an overview | ScienceDirect Topics

Uncertainty quantification (UQ) is the process of quantifying the uncertainties in quantity-of-interest (QoI) by propagating the uncertainties in input ...

MIT Uncertainty Quantification Group: Home

Research Overview. Our research focuses on advancing foundational computational methodologies for uncertainty quantification, statistical inference, and machine ...

Uncertainty Quantification – Center for Computing Research (CCR)

Uncertainty Quantification (UQ) is a growing area of importance for quantifying confidence in computational engineering simulations.

Verification, Validation, & Uncertainty Quantification (VVUQ)

Sources and Types of Uncertainty. Uncertainty Quantification tracks how uncertain inputs result in distributions of outputs. ... Many sources of uncertainty may ...

Uncertainty Quantification: Theory, Implementation, and Applications ...

This book provides a comprehensive and unified treatment of the mathematical, statistical, and computational theory and methods employed to quantify ...

Uncertainty quantification - van der Schaar Lab

Our lab treats the problem of uncertainty quantification itself as an important research pillar in its own right.

Uncertainty Quantification Module - COMSOL

The Uncertainty Quantification Module is used for understanding the impact of model uncertainty — how the quantities of interest depend on variations in the ...

An Introduction to Uncertainty Quantification for Modeling & Simulation

Predictions from modeling and simulation (M&S) are increasingly relied upon to inform critical decision making in a variety of industries ...

Basic Framework and Main Methods of Uncertainty Quantification

This paper gives a comprehensive review on the goals, ideas, and principle methods for each of the UQ processes.

Uncertainty Quantification in Machine Learning for Engineering ...

Abstract page for arXiv paper 2305.04933: Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A ...

The importance of uncertainty quantification in model reproducibility

We argue that uncertainty quantification is crucial for computer model validation and reproducibility.

Uncertainty Quantification: Theory, Implementation, and Applications

Provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models.

UQLab - The Framework for Uncertainty Quantification

In a nutshell. UQLab is a general purpose Uncertainty Quantification framework developed at ETH Zurich (Switzerland). It is made ...

Uncertainty Quantification for Mathematical Biology - ICERM

This workshop capitalizes on the significant potential for mutual advancement in UQ and mathematical biology through cross-disciplinary collaboration.

Uncertainty quantification for probabilistic machine learning in earth ...

Unlike previous uncertainty quantification methods, conformal prediction offers statistically valid prediction regions while concurrently supporting any ...

Uncertainty Quantification 360

UQ360 is an open source toolkit that can help you estimate, understand and communicate uncertainty in machine learning model predictions through AI ...

The Importance of Uncertainty Quantification for Deep Learning ...

This article will focus on building predictive models that surrogate more resource-intensive CAE models and uncertainty quantification in deep learning for ...

Uncertainty quantification | The Alan Turing Institute

UQ is now the key methodology in understanding scientific models, enabling theory and intuition to be rigorously tested and then exploited to provide ...

Uncertainty quantification - Department of Mathematics

Uncertainty quantification (UQ) is a modern inter-disciplinary science that combines statistics, numerical analysis and computational applied mathematics. Today ...