
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.
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