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SIAM Conference on Uncertainty Quantification (UQ26)

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).

Uncertainty quantification (UQ) is essential for establishing the reliability of predictions made by computational models. Such models are often deterministic, and the UQ discipline has traditionally focused on how to quantify uncertainty in that case. Statistical methods play a major role in that effort, and represent an alternative modeling strategy when less is known about the system of interest. The two approaches are naturally synergistic, and with the emergence of machine learning as a practical tool, it is becoming more important than ever to view the whole UQ ecosystem through a unifying lens.

UQ is application-driven and inherently interdisciplinary, relying on a broad range of mathematical and statistical foundations, domain knowledge, and algorithmic and computational tools. UQ26 will bring together mathematicians, statisticians, scientists, engineers, and others interested in the theory, development, and application of UQ methods. Major conference themes will include mathematical and statistical foundations, data-driven approaches and computational advances, applications of UQ in biology, medicine, environmental and climate sciences, decision making for societal benefit, and all areas of physical science and engineering. 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.

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Included Themes

Major Themes

  • Computational advances
  • Data-driven methods
  • Machine learning for UQ and UQ for machine learning
  • Mathematical and statistical foundations of UQ
  • UQ for decision making and societal benefit
  • UQ in biosciences, bioengineering, and biomedicine
  • UQ in climate and environmental sciences
  • UQ in engineering
  • UQ in physical sciences

Subtopics

  • Adversarial approaches to UQ
  • Combining physical and statistical modeling
  • Computer experiments and spatial statistical methods
  • Data assimilation and data fusion
  • Design of experiments
  • Gaussian process modeling and prediction
  • High-dimensional approximation and dimension reduction
  • Infinite-dimensional analysis and approximation
  • Inverse problems
  • Model discrepancy
  • Multiscale, multilevel, and multifidelity UQ methods
  • Optimization
  • Physics-informed machine learning
  • Rare and extreme events
  • Reduced-order models
  • Remote sensing
  • Risk analysis based on UQ
  • Surrogate models and emulators
  • UQ for complex and coupled systems
  • UQ for digital twins
  • UQ for spatial and spatio-temporal processes
  • UQ-informed policy and decisions
  • Verification and validation

Organizing Committee Co-Chairs

Robert Gramacy

Virginia Tech, U.S.

Robert Scheichl

Heidelberg University, Germany

Li Wang

University of Minnesota, U.S.

Organizing Committee

Maarten V. de Hoop

Rice University, U.S.

Mengyang Gu

University of California, Santa Barbara, U.S.

Dorit Hammerling

Colorado School of Mines, U.S.

John Jakeman

Sandia National Laboratories, U.S.

Annika Lang

Chalmers University of Technology, Sweden

Olga Mula

Eindhoven University of Technology, Netherlands

Houman Owhadi

California Institute of Technology, U.S.

Judith Rousseau

University of Oxford, United Kingdom

Elaine Spiller

Marquette University, U.S.

Panagiotis Tsilifis

General Electric, U.S.

Jonathan Weare

Courant Institute, New York University, U.S.

Karen Wilcox

University of Texas at Austin, U.S.

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Funding Agency Support

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

Conference Policies and Guidelines

Attendees should abide by the SIAM Code of Conduct and other conference policies and guidelines. Read all of SIAM's conference guidelines and policies, including the Statement on Potentially Offensive Material.