Practical UQ for Engineering Applications with DAKOTA
Abstract not provided.
Publications
Evaluation of Mixed Continuous-Discrete Surrogate Approaches
Abstract not provided.
Bayesian Model Selection Between Underdetermined Nuclear Reactor Models
Abstract not provided.
Importance Sampling for Rare Event Estimation with Computer Experiments
Abstract not provided.
Optimization of large-scale heterogeneous system-of-systems models
Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.
Use of a SPAR-H Bayesian Network for predicting Human Error Probabilities with missing observations
Abstract not provided.
An initial comparison of methods for representing and aggregating experimental uncertainties involving sparse data
Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
This paper discusses the handling and treatment of uncertainties corresponding to relatively few data samples in experimental characterization of random quantities. The importance of this topic extends beyond experimental uncertainty to situations where the derived experimental information is used for model validation or calibration. With very sparse data it is not practical to have a goal of accurately estimating the underlying variability distribution (probability density function, PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a desired percentage of the actual PDF, say 95% included probability, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the random-variable range corresponding to the desired percentage of the actual PDF. The performance of a variety of uncertainty representation techniques is tested and characterized in this paper according to these two opposing objectives. An initial set of test problems and results is presented here from a larger study currently underway.
Decision making under epistemic uncertainty for a complex mechanical system
Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
This paper explores various frameworks to quantify and propagate sources of epistemic and aleatoric uncertainty within the context of decision making for assessing system performance relative to design margins of a complex mechanical system. If sufficient data is available for characterizing aleatoric-type uncertainties, probabilistic methods are commonly used for computing response distribution statistics based on input probability distribution specifications. Conversely, for epistemic uncertainties, data is generally too sparse to support objective probabilistic input descriptions, leading to either subjective probabilistic descriptions (e.g., assumed priors in Bayesian analysis) or non-probabilistic methods based on interval specifications. Among the techniques examined in this work are (1) Interval analysis, (2) Dempster-Shafer Theory of Evidence, (3) a second-order probability (SOP) analysis in which the aleatory and epistemic variables are treated separately, and a nested iteration is performed, typically sampling epistemic variables on the outer loop, then sampling over aleatory variables on the inner loop and (4) a Bayesian approach where plausible prior distributions describing the epistemic variable are created and updated using available experimental data. This paper compares the results and the information provided by different methods to enable decision making in the context of performance assessment when epistemic uncertainty is considered.
DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification 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 flexible 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 quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.
Uncertainty Quantification: Part 3. Presentation at the Risk Informed Safety Margin Characterization workshop
Abstract not provided.
First-order reduced-space formulations for parameter estimation within the frameworks of steady state dynamics: The elastic and viscoelastic case
Inverse Problems
Abstract not provided.
Multiple Model Inference: Calibration Selection and Prediction with Multiple Models
Abstract not provided.
Reliability Methods: Determining small failure probabilities
Abstract not provided.
"Mixed" Epistemic-Aleatory UQ using Stochastic Expansion methods
Abstract not provided.
Evaluation of Mixed Continuous-Discrete Surrogate Approaches
Abstract not provided.
Multiple Model Inference: Calibration and Selection with Multiple Models
Abstract not provided.
An Initial Comparison of Methods for Representing and Aggregating Experimental Uncertainties involving Sparse Data
Abstract not provided.
Managing Complexity in Simulation-Based Uncertainty Quantification
Abstract not provided.
Multiple Model Inference: Calibration Selection and Prediction with Multiple Models
Abstract not provided.
Mixed Variable Surrogate Models for Engineering Analysis
Abstract not provided.
Evaluation of Mixed Continuous-Discrete Surrogate Approaches
Abstract not provided.
Uncertainty Quantification: Introduction
Abstract not provided.
Sensitivity analysis techniques applied to a model shock problem
Abstract not provided.
Sensitivity analysis techniques applied to a model shock problem
Abstract not provided.
Surrogate modeling with surfpack
Surfpack is a library of multidimensional function approximation methods useful for efficient surrogate-based sensitivity/uncertainty analysis or calibration/optimization. I will survey current Surfpack meta-modeling capabilities for continuous variables and describe recent progress generalizing to both continuous and categorical factors, including relevant test problems and analysis comparisons.
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