Overview of Verification Validation Uncertainty Quantification and QMU
Abstract not provided.
Publications
Perspectives on verification, validation, and quantification of margins and uncertainties
Abstract not provided.
Informatics : the rest of the story
Abstract not provided.
An Overview of Sensitivity Analysis and Uncertainty Quantification Methods
Abstract not provided.
Perspective of Predictive Science in the NNSA Advanced Simulation and Computing Program
Abstract not provided.
Aleatory and Epistemic Uncertainty Quantification for Engineering Applications
Abstract not provided.
Aleatory and epistemic uncertainty quantification for engineering applications
Abstract not provided.
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 developers manual
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 finite element 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 developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes.
DAKOTA, a multilevel parellel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 uers's manual
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 finite element 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 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 and its use in Computational Experiments
Abstract not provided.
DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 reference manual
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 finite element 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 reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
The Surfpack Software Library for Surrogate Modeling of Sparse Irregularly Spaced Multidimensional Data
Abstract not provided.
Methodology Status and Needs: Verification Validation and Uncertainty Quantification
Abstract not provided.
Bayesian Calibration of the QASPR Model
Abstract not provided.
An Overview of Sensitivity Analysis and Uncertainty Quantification Methods
Abstract not provided.
Validation and Uncertainty Quantification Methods in the DAKOTA Software Toolkit
Abstract not provided.
The DAKOTA Toolkit and its use in Computational Experiments
Abstract not provided.
Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions
Many engineering application problems use optimization algorithms in conjunction with numerical simulators to search for solutions. The formulation of relevant objective functions and constraints dictate possible optimization algorithms. Often, a gradient based approach is not possible since objective functions and constraints can be nonlinear, nonconvex, non-differentiable, or even discontinuous and the simulations involved can be computationally expensive. Moreover, computational efficiency and accuracy are desirable and also influence the choice of solution method. With the advent and increasing availability of massively parallel computers, computational speed has increased tremendously. Unfortunately, the numerical and model complexities of many problems still demand significant computational resources. Moreover, in optimization, these expenses can be a limiting factor since obtaining solutions often requires the completion of numerous computationally intensive simulations. Therefore, we propose a multifidelity optimization algorithm (MFO) designed to improve the computational efficiency of an optimization method for a wide range of applications. In developing the MFO algorithm, we take advantage of the interactions between multi fidelity models to develop a dynamic and computational time saving optimization algorithm. First, a direct search method is applied to the high fidelity model over a reduced design space. In conjunction with this search, a specialized oracle is employed to map the design space of this high fidelity model to that of a computationally cheaper low fidelity model using space mapping techniques. Then, in the low fidelity space, an optimum is obtained using gradient or non-gradient based optimization, and it is mapped back to the high fidelity space. In this paper, we describe the theory and implementation details of our MFO algorithm. We also demonstrate our MFO method on some example problems and on two applications: earth penetrators and groundwater remediation.
The surfpack software library for surrogate modeling of sparse irregularly spaced multidimensional data
Collection of Technical Papers - 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
Surfpack is a general-purpose software library of multidimensional function approximation methods for applications such as data visualization, data mining, sensitivity analysis, uncertainty quantification, and numerical optimization. Surfpack is primarily intended for use on sparse, irregularly-spaced, n-dimensional data sets where classical function approximation methods are not applicable. Surfpack is under development at Sandia National Laboratories, with a public release of Surfpack version 1.0 in August 2006. This paper provides an overview of Surfpack's function approximation methods along with some of its software design attributes. In addition, this paper provides some simple examples to illustrate the utility of Surfpack for data trend analysis, data visualization, and optimization. Copyright © 2006 by the American Institute of Aeronautics and Astronautics, Inc.
Perspectives on optimization under uncertainty: Algorithms and applications
This paper provides an overview of several approaches to formulating and solving optimization under uncertainty (OUU) engineering design problems. In addition, the topic of high-performance computing and OUU is addressed, with a discussion of the coarse- and fine-grained parallel computing opportunities in the various OUU problem formulations. The OUU approaches covered here are: sampling-based OUU, surrogate model-based OUU, analytic reliability-based OUU (also known as reliability-based design optimization), polynomial chaos-based OUU, and stochastic perturbation-based OUU.
Advanced uncertainty quantification methods for engineering applications
Abstract not provided.
The promise and peril of uncertainty quantification using response surface approximations
Proposed for publication in Structure and Infrastructure Engineering.
Abstract not provided.
Shockless magnetic acceleration of al flyer plates to ultra-high velocity using multi-megabar drive pressures
The intense magnetic field generated in the 20 MA Z-machine is used to accelerate metallic flyer plates to high velocity for the purpose of generating strong shocks in equation of state experiments. We present results pertaining to experiments in which a 0.085 cm thick Al flyer plate is magnetically accelerated across a vacuum gap into a quartz target. Peak magnetic drive pressures up to 4.9 Mbar were produced, which yielded a record 34 km/s flyer velocity without destroying it by shock formation or Joule heating. Two-dimensional MHD simulation was used to optimize the magnetic drive pressure on the flyer surface, shape the current pulse to accelerate the flyer without shock formation (i.e., quasi-isentropically), and predict the flyer velocity. Shock pressures up to 11.5 Mbar were produced in quartz. Accurate measurements of the shock velocity indicate that a fraction of the flyer is at solid density when it arrives at the target. Comparison of measurements and simulation results yields a consistent picture of the flyer state at impact with the quartz target.
A new scheme for multifidelity optimization incorporating pattern search and space mapping
Abstract not provided.
Optimization under uncertainty research and applications at Sandia National Laboratories
Abstract not provided.
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