Publications

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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.

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.

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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, analytic reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity 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.

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, analytic reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity 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.

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, analytic reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity 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.

Single-level parallel optimization approaches, those in which either the simulation code executes in parallel or the optimiza- tion algorithm invokes multiple simultaneous single-processor analyses, have been investigated previously and been shown to be effective in reducing the time required to compute optimal solutions. However, these approaches have clear performance limita- tions that prevent effective scaling with the thousands of processors available in massively parallel supercomputers. In more recent work, a capability has been developed for multilevel parallelism in which multiple instances of multiprocessor simulations are coordinated simultaneously. This implementation employs a master-slave approach using the Message Passing Interface (MPI) within the DAKOTA software toolkit. Mathematical analysis on achieving peak efficiency in multilevel parallelism has shown that the most effective processor partitioning scheme is the one that limits the size of multiprocessor simulations in favor of concurrent execution of multiple simulations. That is, if both coarse-grained and fine-grained parallelism can be exploited, then preference should be given to the coarse-grained parallelism. This analysis was verified in multilevel paralIel computatiorud experiments on networks of workstations (NOWS) and on the Intel TeraFLOPS massively parallel supercomputer. In current work, methods for exploiting additional coarse-grained parallelism in optimization are being investigated so that fine-grained efficiency losses can be further minimized. These activities are focusing on both algorithmic coarse-grained parallel- ism (multiple independent function evaluations) through the development of speculative gradient methods and concurrent iterator strategies and on function evaluation coarse-grained parallelism (multiple separable simulations within a function evaluation) through the development of general partitioning and nested synchronization facilities. The net result is a total of four separate lev- els of parallelism which can minimize efficiency losses and achieve near linear scaling on massively parallel computers.

LDRD research activities have focused on increasing the robustness and efficiency of optimization studies for computationally complex engineering problems. Engineering applications can be characterized by extreme computational expense, lack of gradient information, discrete parameters, non-converging simulations, and nonsmooth, multimodal, and discontinuous response variations. Guided by these challenges, the LDRD research activities have developed application-specific techniques, fundamental optimization algorithms, multilevel hybrid and sequential approximate optimization strategies, parallel processing approaches, and automatic differentiation and adjoint augmentation methods. This report surveys these activities and summarizes the key findings and recommendations.

An analytical and experimental study is conducted to investigate the effect of isolator locations on the effectiveness of vibration isolation systems. The study uses isolators with fixed properties and evaluates potential improvements to the isolation system that can be achieved by optimizing isolator locations. Because the available locations for the isolators are discrete in this application, a Genetic Algorithm (GA) is used as the optimization method. The system is modeled in MATLAB{trademark} and coupled with the GA available in the DAKOTA optimization toolkit under development at Sandia National Laboratories. Design constraints dictated by hardware and experimental limitations are implemented through penalty function techniques. A series of GA runs reveal difficulties in the search on this heavily constrained, multimodal, discrete problem. However, the GA runs provide a variety of optimized designs with predicted performance from 30 to 70 times better than a baseline configuration. An alternate approach is also tested on this problem: it uses continuous optimization, followed by rounding of the solution to neighboring discrete configurations. Results show that this approach leads to either infeasible or poor designs. Finally, a number of optimized designs obtained from the GA searches are tested in the laboratory and compared to the baseline design. These experimental results show a 7 to 46 times improvement in vibration isolation from the baseline configuration.