SIAM Conference on Uncertainty Quantification

SIAM Conference

Hybrid: SIAM Conference on Uncertainty Quantification (UQ22)

April 12 - 15, 2022

Westin Peachtree Plaza | Atlanta, Georgia, U.S.

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About the Conference

About the Conference


This is the conference of the SIAM Activity Group on Uncertainty Quantification.

This conference is being held in cooperation with the American Statistical Association (ASA) and GAMM Activity Group on Uncertainty Quantification (GAMM AG UQ).

Uncertainty quantification (UQ) is essential for establishing the predictive accuracy of computational models for essentially all fields of science and engineering. The recent pandemic has highlighted the importance of quantifying uncertainty when working with potentially inaccurate models and insufficient data. UQ is an inherently interdisciplinary field based on a broad range of mathematical and statistical foundational topics and associated algorithmic and computational developments. UQ22 will bring together mathematicians, statisticians, scientists, and engineers interested in the theory, development, and implementation of UQ methods. Whereas a broad range of topics will be represented, major conference themes will include mathematical and statistical foundations of UQ, model-informed and data-driven UQ approaches, and applications of UQ in the biological, medical, climate, and physical sciences. The goal of the conference is to provide a forum for exchanging ideas between diverse groups from academia, industry, and government laboratories, thereby enhancing communication and contributing to future advances in the field.

Included Themes

Included Themes

Major Themes

  • Data-driven and model-informed UQ methods
  • Mathematical and statistical foundations of UQ
  • UQ in biosciences, bioengineering, and biomedicine
  • UQ in climate and environmental sciences
  • UQ in threat and risk management


  • Adversarial approaches to UQ
  • Connections between UQ and Machine Learning
  • Data assimilation
  • Data-driven and machine learning techniques for model discrepancy
  • Design of experiments
  • High-dimensional approximation
  • Infinite-dimensional analysis and approximation
  • Inverse problems
  • Multiscale UQ methods
  • Optimization and control under uncertainty
  • Physics-informed machine learning for UQ
  • Prediction in the presence of model discrepancy
  • Rare and extreme events
  • Reduced-order models
  • Remote sensing
  • Risk analysis based on UQ
  • Statistical methods for UQ
  • Stochastic models
  • Surrogate models and high-dimensional emulators
  • UQ for complex and coupled systems
  • UQ-informed policy
  • Verification and validation

Conference Committees

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Conference Sponsors

Funding Agency

SIAM and the Organizing Committee wish to extend their thanks and appreciation to the U.S. National Science Foundation for its support of this conference.

Ways to Sponsor

SIAM invites you to show support of this meeting through sponsorship opportunities ranging from support of receptions, audio-video needs, to awards for student travel, and more.

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Thank you to our sponsors

Thank you to our conference sponsors

Thank you to our sponsors

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