Media Summary: Jonas Schulz from the Technical University of Dresden provided a presentation entitled " Neural networks are infamous for making wrong predictions with high confidence. Ideally, when a model encounters difficult ... Calibration has emerged as a standard approach to

Uncertainty Quantification Surrogate Building And - Detailed Analysis & Overview

Jonas Schulz from the Technical University of Dresden provided a presentation entitled " Neural networks are infamous for making wrong predictions with high confidence. Ideally, when a model encounters difficult ... Calibration has emerged as a standard approach to So the idea is to do a sequential sampling so first you sample some points this this first step is just like to Gaussian process regression (GPR) is a probabilistic approach to making predictions. GPRs are easy to implement, flexible, and ... Presented at the Argonne Training Program on Extreme-Scale Computing 2019. Slides for this presentation are available here: ...

Predictions from modeling and simulation (M&S) are increasingly relied upon to inform critical decision making in a variety of ... Talk by Marko Jarvenpaa at the One World ABC Seminar on October 1 2020. For more information on the seminar series, see ... NYU CUSP's Research Seminar Series features leading voices in the growing field of urban informatics. Check out upcoming ...

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Uncertainty quantification, surrogate building and active learning
NLDL2022 "Uncertainty Quantification of Surrogate Explanations" by Jonas Schulz (TU Dresden)
Quantifying the Uncertainty in Model Predictions
Charlotte Peale: Uncertainty Quantification Beyond Calibration (February 5, 2026)
Gaussian Process Based Surrogate Models
Easy introduction to gaussian process regression (uncertainty models)
Why Use Uncertainty Quantification?
Uncertainty Quantification and Deep Learning ǀ Elise Jennings, Argonne National Laboratory
Surrogate Models for Uncertainty Quantification presented by Dr. Ralph Smith, NCSU
Mini Tutorial 6:  An Introduction to Uncertainty Quantification for Modeling & Simulation
Batch simulations and uncertainty quantification in Gaussian process surrogate ABC
Using machine learning & uncertainty quantification to tackle data in high-res disaster simulations
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Uncertainty quantification, surrogate building and active learning

Uncertainty quantification, surrogate building and active learning

U. von Toussaint (IPP)

NLDL2022 "Uncertainty Quantification of Surrogate Explanations" by Jonas Schulz (TU Dresden)

NLDL2022 "Uncertainty Quantification of Surrogate Explanations" by Jonas Schulz (TU Dresden)

Jonas Schulz from the Technical University of Dresden provided a presentation entitled "

Quantifying the Uncertainty in Model Predictions

Quantifying the Uncertainty in Model Predictions

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

Charlotte Peale: Uncertainty Quantification Beyond Calibration (February 5, 2026)

Charlotte Peale: Uncertainty Quantification Beyond Calibration (February 5, 2026)

Calibration has emerged as a standard approach to

Gaussian Process Based Surrogate Models

Gaussian Process Based Surrogate Models

So the idea is to do a sequential sampling so first you sample some points this this first step is just like to

Easy introduction to gaussian process regression (uncertainty models)

Easy introduction to gaussian process regression (uncertainty models)

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

Why Use Uncertainty Quantification?

Why Use Uncertainty Quantification?

An overview of how

Uncertainty Quantification and Deep Learning ǀ Elise Jennings, Argonne National Laboratory

Uncertainty Quantification and Deep Learning ǀ Elise Jennings, Argonne National Laboratory

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

Surrogate Models for Uncertainty Quantification presented by Dr. Ralph Smith, NCSU

Surrogate Models for Uncertainty Quantification presented by Dr. Ralph Smith, NCSU

Surrogate

Mini Tutorial 6:  An Introduction to Uncertainty Quantification for Modeling & Simulation

Mini Tutorial 6: 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 ...

Batch simulations and uncertainty quantification in Gaussian process surrogate ABC

Batch simulations and uncertainty quantification in Gaussian process surrogate ABC

Talk by Marko Jarvenpaa at the One World ABC Seminar on October 1 2020. For more information on the seminar series, see ...

Using machine learning & uncertainty quantification to tackle data in high-res disaster simulations

Using machine learning & uncertainty quantification to tackle data in high-res disaster simulations

NYU CUSP's Research Seminar Series features leading voices in the growing field of urban informatics. Check out upcoming ...

Surrogate modelling - linking uncertainty quantification and engineering design - open discussion

Surrogate modelling - linking uncertainty quantification and engineering design - open discussion

DAWS Workshop on