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June 15–26, 2015 New Directions Short Course ORGANIZERS Youssef Marzouk, Massachusetts Institute of Technology Luis Tenorio, Colorado School of Mines GUEST LECTURERS Mark Berliner , The Ohio State University Julianne Chung, Virginia Polytechnic Institute and State University Paul Constantine, Colorado School of Mines Colin Fox, University of Otago Omar Ghattas, University of Texas, Austin Robert Moser , University of Texas, Austin Akil Narayan, University of Massachusetts Dartmouth Juan Restrepo, Oregon State University Chris Snyder , University Corporation for Atmospheric Research (UCAR) Laura Swiler , Sandia National Laboratories New Directions Short Course Introduction to Uncertainty Quantification Uncertainty quantification (UQ) lies at the confluence of many fields, including probability, statistics, data analysis, computational mathematics, and mathematical modeling. It touches upon almost any field that involves models, whether of natural or engineered systems, and data. The objective of the course is to help participants from different backgrounds obtain the basic understanding and tools needed to use UQ in their fields of interest. In addition, the course will present snapshots of current research and examples of UQ in different application areas, so that participants are aware of the types of methods that are in current use. This course will be of interest to people involved in science and engineering applications and information-based decision making, and it will also be of interest to people who want to understand the fundamental mathematical ideas and tools underlying this endeavor. www.ima.umn.edu/2014-2015/ND6.15-26.15 The IMA is a NSF-funded institute The following lectures will be delivered over the two-week summer school: Core Material Probability and statistical inference for UQ Sensitivity analysis Gaussian processes and computer model emulation Spectral methods for UQ High-dimensional approximation Monte Carlo and MCMC methods Model validation Advanced Topics & Applications Bayesian methods for geophysical applications Large-scale statistical inverse problems Data assimilation Model inadequacy Optimal experimental design

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Page 1: New Directions Short Course Introduction to Uncertainty ... · Introduction to Uncertainty Quantification Uncertainty quantification (UQ) lies at the confluence of many fields, including

June 15–26, 2015

New Directions

Short Course

ORGANIZERS

Youssef Marzouk, Massachusetts Institute of TechnologyLuis Tenorio, Colorado School of Mines

GUEST LECTURERS

Mark Berliner, The Ohio State UniversityJulianne Chung, Virginia Polytechnic Institute and State UniversityPaul Constantine, Colorado School of Mines Colin Fox, University of OtagoOmar Ghattas, University of Texas, AustinRobert Moser, University of Texas, AustinAkil Narayan, University of Massachusetts DartmouthJuan Restrepo, Oregon State UniversityChris Snyder, University Corporation for Atmospheric Research (UCAR)Laura Swiler, Sandia National Laboratories

New Directions Short Course

Introduction to Uncertainty Quantification

Uncertainty quantification (UQ) lies at the confluence of many fields, including probability, statistics, data analysis, computational mathematics, and mathematical modeling. It touches upon almost any field that involves models, whether of natural or engineered systems, and data. The objective of the course is to help participants from different backgrounds obtain the basic understanding and tools needed to use UQ in their fields of interest. In addition, the course will present snapshots of current research and examples of UQ in different application areas, so that participants are aware of the types of methods that are in current use. This course will be of interest to people involved in science and engineering applications and information-based decision making, and it will also be of interest to people who want to understand the fundamental mathematical ideas and tools underlying this endeavor.

www.ima.umn.edu/2014-2015/ND6.15-26.15

The IMA is a NSF-funded institute

The following lectures will be delivered over the two-week summer school:

Core Material Probability and statistical inference for UQSensitivity analysisGaussian processes and computer model emulationSpectral methods for UQHigh-dimensional approximationMonte Carlo and MCMC methodsModel validation

Advanced Topics & Applications Bayesian methods for geophysical applicationsLarge-scale statistical inverse problemsData assimilationModel inadequacyOptimal experimental design

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FASTMath: UQ Software · 2019. 8. 14. · FASTMath: UQ Software The SciDACFASTMath uncertainty quantification (UQ) team works on development of robust UQ methods within high-quality

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