ViewTube

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

Gaussian process regression (GPR) is a probabilistic approach to making predictions. GPRs are easy to implement, flexible, and ...

A brief overview of Uncertainty Quantification (UQ) and how it can help determine variations due to real world situations.

2025 ML Academy & Artiste Distinguished Lecture.

This is a quick video brief on a new paper published by Ni Zhan and myself on uncertainty quantification in machine learning and ...

Module 8.1 introduction to uncertainty quantification methods in this method we'll provide a basic understanding of what is ukq ...

A quick 20 min introduction to various UQ methods for Deep Learning:- - Why is UQ required for Deep Learning - Bayesian NN ...

Neural networks are infamous for making wrong predictions with high confidence. Ideally, when a model encounters difficult ...

www.pydata.org Uncertainty identification in machine learning is crucial for making robust decisions, enhancing model ...

Presented at the Argonne Training Program on Extreme-Scale Computing 2019. Slides for this presentation are available here: ...

In seismic inverse problems, the noise and illumination configuration severely impact the interpretation of the subsurface.

THURSDAY, FEBRUARY 11 @ 2PM PT Uncertainty Quantification in Computational Models of Physical Systems Habib Najm, ...

This paper takes a fully probabilistic approach by modeling the joint distribution over questions and inputs, defining uncertainty as ...

Recorded 11 March 2026. John Jakeman of Sandia National Laboratories presents "Forward Uncertainty Quantification" at IPAM's ...

Roger Ghanem is Professor of Civil and Environmental Engineering at the U of Southern California where he also holds the Tryon ...

Okay so now I will talk about the main part of the talk where I will talk about practical methods for uncertainty quantification in these ...