Publications

Results 126–150 of 219

Search results

Jump to search filters

Whitepaper prepared for DOE Workshop on Integrated Simulations for Magnetic Fusion Energy Sciences: Topic E: Beyond interpretive simulations

Swiler, Laura P.; Eldred, Michael S.; Shadid, John N.

Predictive simulations of magnetic confinement fusion and burning plasmas will require the quantification of all uncertainties and errors relating to the simulation capabilities. These include: discretization error (temporal and spatial); incomplete convergence error (nonlinear, linear, etc.); uncertainties in input data (initial conditions, boundary conditions, coefficients, thermo-physical properties, source terms, etc.); and uncertainties in the component models (the specific form and parameter values).13 Typically, there is a focus on model parameter uncertainties but uncertainties about which model to use and about the bias or discrepancy of a particular model (sometimes called model form error) often dominate parameter uncertainty. This whitepaper addresses the challenges of performing uncertainty quantification (UQ) for expensive computational models.

More Details
TYPE Other Report YEAR 2015
DOIOSTI

Probabilistic methods for sensitivity analysis and calibration in the NASA challenge problem

Journal of Aerospace Information Systems

Safta, Cosmin S.; Sargsyan, Khachik S.; Najm, H.N.; Chowdhary, Kenny; Debusschere, Bert D.; Swiler, Laura P.; Eldred, Michael S.

In this paper, a series of algorithms are proposed to address the problems in the NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters based on the available data, whereas a variance-based global sensitivity analysis is used to rank the epistemic and aleatory model parameters. A nested sampling of the aleatory-epistemic space is proposed to propagate uncertainties from model parameters to output quantities of interest.

More Details
TYPE Journal Article YEAR 2015
ScopusOSTIDOI

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

Adams, Brian M.; Jakeman, John D.; Swiler, Laura P.; Stephens, John A.; Vigil, Dena V.; Wildey, Timothy M.; Bauman, Lara E.; Bohnhoff, William J.; Dalbey, Keith D.; Eddy, John P.; Ebeida, Mohamed S.; Eldred, Michael S.; Hough, Patricia D.; Hu, Kenneth H.

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

More Details
TYPE SAND Report YEAR 2014
DOIOSTI

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

Adams, Brian M.; Jakeman, John D.; Swiler, Laura P.; Stephens, John A.; Vigil, Dena V.; Wildey, Timothy M.; Bauman, Lara E.; Bohnhoff, William J.; Dalbey, Keith D.; Eddy, John P.; Ebeida, Mohamed S.; Eldred, Michael S.; Hough, Patricia D.; Hu, Kenneth H.

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

More Details
TYPE SAND Report YEAR 2014
DOIOSTI

Uncertainty quantification methods for model calibration validation, and risk analysis

16th AIAA Non-Deterministic Approaches Conference

Sargsyan, Khachik S.; Najm, H.N.; Chowdhary, Kamaljit S.; Debusschere, Bert D.; Swiler, Laura P.; Eldred, Michael S.

In this paper we propose a series of methodologies to address the problems in the NASA Langley Multidisciplinary UQ Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters in problem A, while variance-based global sensitivity analysis is proposed for problem B. For problems C and D we propose nested sampling methods for mixed aleatory-epistemic UQ.

More Details
TYPE Conference YEAR 2014
DOIOSTIDOIScopusOSTIScopus
Results 126–150 of 219
Results 126–150 of 219