Context Starter: LatinX in AI (LXA) at NeurIPS 2021: Author: Matias Valdenegro-Toro The workshop is a one-day event with invited speakers, oral ... High-fidelity simulation capabilities have progressed rapidly over the past decades in computational fluid dynamics (CFD), ...

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LatinX in AI (LXA) at NeurIPS 2021: Author: Matias Valdenegro-Toro The workshop is a one-day event with invited speakers, oral ... Predictions from modeling and simulation (M&S) are increasingly relied upon to inform critical decision making in a variety of ... Bobby Gramacy is a Professor of Statistics at Virginia Tech and a Fellow of the American Statistical ...

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Bobby Gramacy is a Professor of Statistics at Virginia Tech and a Fellow of the American Statistical ... High-fidelity simulation capabilities have progressed rapidly over the past decades in computational fluid dynamics (CFD), ...

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Pau is a PhD student in Computing and Mathematical Sciences at Caltech, advised by Houman Owhadi. Jonas Schulz from the Technical University of Dresden provided a presentation entitled "

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  • Jonas Schulz from the Technical University of Dresden provided a presentation entitled "
  • High-fidelity simulation capabilities have progressed rapidly over the past decades in computational fluid dynamics (CFD), ...
  • Bobby Gramacy is a Professor of Statistics at Virginia Tech and a Fellow of the American Statistical ...
  • Predictions from modeling and simulation (M&S) are increasingly relied upon to inform critical decision making in a variety of ...

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Helpful Visuals

Uncertainty quantification, surrogate building and active learning
Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings
Mini Tutorial 6:  An Introduction to Uncertainty Quantification for Modeling & Simulation
Uncertainty Quantification and Deep Learning ǀ Elise Jennings, Argonne National Laboratory
Surrogate Modeling and Active Learning for Optimization | Fireside Chat with Dr. Bobby Gramacy
IDS PhD-Teach-PhD Workshops 2022 - Uncertainty Quantification for Reliable Machine Learning
Charlotte Peale: Uncertainty Quantification Beyond Calibration (February 5, 2026)
NLDL2022 "Uncertainty Quantification of Surrogate Explanations" by Jonas Schulz (TU Dresden)
A Data Driven Approach of Uncertainty Quantification on Reynolds Stress Based On DNS Turbulence Data
Epistemic and Aleatoric Uncertainty Quantification for Gaussian Processes
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Open Topic Snapshot
Uncertainty quantification, surrogate building and active learning

Uncertainty quantification, surrogate building and active learning

Read more details and related context about Uncertainty quantification, surrogate building and active learning.

Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings

Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings

LatinX in AI (LXA) at NeurIPS 2021: Author: Matias Valdenegro-Toro The workshop is a one-day event with invited speakers, oral ...

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 ...

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

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

Read more details and related context about Uncertainty Quantification and Deep Learning ǀ Elise Jennings, Argonne National Laboratory.

Surrogate Modeling and Active Learning for Optimization | Fireside Chat with Dr. Bobby Gramacy

Surrogate Modeling and Active Learning for Optimization | Fireside Chat with Dr. Bobby Gramacy

Thought Leader: Dr. Bobby Gramacy is a Professor of Statistics at Virginia Tech and a Fellow of the American Statistical ...

IDS PhD-Teach-PhD Workshops 2022 - Uncertainty Quantification for Reliable Machine Learning

IDS PhD-Teach-PhD Workshops 2022 - Uncertainty Quantification for Reliable Machine Learning

Read more details and related context about IDS PhD-Teach-PhD Workshops 2022 - Uncertainty Quantification for Reliable Machine Learning.

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

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

Read more details and related context about Charlotte Peale: Uncertainty Quantification Beyond Calibration (February 5, 2026).

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 "

A Data Driven Approach of Uncertainty Quantification on Reynolds Stress Based On DNS Turbulence Data

A Data Driven Approach of Uncertainty Quantification on Reynolds Stress Based On DNS Turbulence Data

High-fidelity simulation capabilities have progressed rapidly over the past decades in computational fluid dynamics (CFD), ...

Epistemic and Aleatoric Uncertainty Quantification for Gaussian Processes

Epistemic and Aleatoric Uncertainty Quantification for Gaussian Processes

Pau is a PhD student in Computing and Mathematical Sciences at Caltech, advised by Houman Owhadi. His main research area ...