DAKOTA Update: Capabilities & Applications
Abstract not provided.
Publications
Sandia Capabilities for Uncertainty Quantification
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Adjoint-Based Sensitivities for Optimization of Satellite Electron/Proton Shields
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Adjoint-enabled Uncertainty Quantification for Satellite Shield Design
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Adjoint-enabled Optimization and UQ for Radiation Shield Design
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Dakota's Role in Verification
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Adjoint-Based Sensitivities for Optimization of Satellite Electron/Proton Shields
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User Guidelines and Best Practices for CASL VUQ Analysis Using Dakota
Sandia's Dakota software (available at http://dakota.sandia.gov) supports science and engineering transformation through advanced exploration of simulations. Specifically, it manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to enhance understanding of risk, improve products, and assess simulation credibility. This manual offers Consortium for Advanced Simulation of Light Water Reactors (LWRs) (CASL) partners a guide to conducting Dakota-based VUQ studies for CASL problems. It motivates various classes of Dakota methods and includes examples of their use on representative application problems. On reading, a CASL analyst should understand why and how to apply Dakota to a simulation problem.
Sensitivity and Uncertainty Analysis with VERA?s Dakota Module
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Recent Advances in Dakota UQ
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Dakota Software Training: Sensitivity Analysis
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Dakota: Algorithms for Design Exploration and Simulation Credibility; Data Analysis Capabilities
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Dakota Software Training: Uncertainty Quantification
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Dakota Software Training: Optimization
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Dakota: Benefits and Challenges of Lab-developed Open Source Scientific Software
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Dakota Software Training: Model Calibration
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Uncertainty Quantification Methods in the Dakota Toolkit
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Bayesian Calibration in Dakota
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Credible Simulation Emphasizing Sensitivity Uncertainty and Calibration
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Sensitivity Analysis (SA) and Uncertainty Quantification (UQ) Tools in Sandia Analysis Workbench (SAW)
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Dakota Sensitivity Analysis and Uncertainty Quantification with Examples
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Overview of selected DOE/NNSA predictive science initiatives: The predictive science academic alliance program and the DAKOTA project
53rd AIAA Aerospace Sciences Meeting
This paper supports a special session on "Frontiers of Uncertainty Management for Com- plex Aerospace Systems" with the intent of summarizing two aspects of the DOE/NNSA Accelerated Strategic Computing (ASC) program, each of which is focused on predictive science using complex simulation models. The first aspect is academic outreach, as enabled by the Predictive Science Academic Alliance Program (PSAAP). The second aspect is the Dakota project at Sandia National Laboratories, which develops and deploys uncertainty quantification capabilities focused on high fidelity modeling and simulation on large-scale parallel computers.
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 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.
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual
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.
ALEGRA Update: Modernization and Resilience Progress
Abstract not provided.
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