An Overview of Select UQ Algorithms and their Utility in Applications
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Publications
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Jump to search filtersEnhancing %601-minimization estimates of polynomial chaos expansions using basis selection
Journal of Computational Physics
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Uncertainty Quantification and Calibration of Physical Models
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Bayesian discontinuity detection and surrogate construction for complex computer models
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BAYESIAN CALIBRATION OF THE COMMUNITY LAND MODEL USING SURROGATES
SIAM Journal of Uncertainty Quantification
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Bayesian calibration of hydrological parameters in the Community Land Model (CODA Presentation)
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Probabilistic Approaches for Fault-Tolerance and Scalability in Extreme-Scale Computing
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Uncertainty Quantification Methods for Model Calibration Validation and Risk Analysis
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Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model
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Uncertainty Quantification Methods for Model Calibration Validation and Risk Analysis
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Inference of Chemical Model Parameters given Partial Information
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Bayesian compressive sensing and dimensionality reduction for high-dimensional models
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Hybrid Discrete / Continuum Algorithms for Stochastic Reaction Networks
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SST/macro Manual
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UQTk version 2.0 user manual
The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 2.0 ffers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.
Uncertainty quantification of reaction mechanisms accounting for correlations introduced by rate rules and fitted Arrhenius parameters
Combustion and Flame
We study correlations among uncertain Arrhenius rate parameters in a chemical model for hydrocarbon fuel-air combustion. We consider correlations induced by the use of rate rules for modeling reaction rate constants, as well as those resulting from fitting rate expressions to empirical measurements arriving at a joint probability density for all Arrhenius parameters. We focus on homogeneous ignition in a fuel-air mixture at constant-pressure. We outline a general methodology for this analysis using polynomial chaos and Bayesian inference methods. We examine the uncertainties in both the Arrhenius parameters and in predicted ignition time, outlining the role of correlations, and considering both accuracy and computational efficiency. © 2013 The Combustion Institute.
Hybrid Discrete - Continuum Algorithms for Stochastic Reaction Networks
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Probabilistic Approaches for Communication Avoidance and Resilience in Exascale Simulations
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Presentation: Validating extreme-scale HPC simulation through Bayesian inference uncertainty quantification
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SST/macro: A coarse-grained simulation workflow
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Sparse surrogate model construction via compressive sensing
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DALEC calibration with daily NEE Data at Harvard Forest
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Parameter Estimation with Partial Information
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UQ tutorial slides
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Parameter Estimation with Missing Data
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