Publications

Orient, George E.; Carnes, Brian C.; Romero, Vicente J.; Swiler, Laura P.; Weirs, Vincent G.

Abstract not provided.

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

Abstract not provided.

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

Abstract not provided.

Swiler, Laura P.; Romero, Vicente J.

Abstract not provided.

Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

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.

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

Abstract not provided.

Rutherford, Brian M.; Romero, Vicente J.; Hoekstra, Robert J.; Castro, Joseph P.

Abstract not provided.

McFadden, Francesca R.; Romero, Vicente J.; Swiler, Laura P.

Abstract not provided.

Conference Proceedings of the Society for Experimental Mechanics Series

Romero, Vicente J.; Slepoy, R.; Swiler, Laura P.; Giunta, A.A.; Krishnamurthy, T.

Response surface functions are often used as simple and inexpensive replacements for computationally expensive computer models that simulate the behavior of a complex system over some parameter space. "Progressive" response surfaces are built up incrementally as global information is added from new sample points added to the previous points in the parameter space. As the response surfaces are globally upgraded, indicators of the convergence of the response surface approximation to the exact (fitted) function can be inferred. Sampling points can be incrementally added in a structured or unstructured fashion. Whatever the approach, it is usually desirable to sample the entire parameter space uniformly (at least in early stages of sampling). At later stages of sampling, depending on the nature of the quantity being resolved, it may be desirable to continue sampling uniformly (progressive response surfaces), or to switch to a focusing/economizing strategy of preferentially sampling certain regions of the parameter space based on information gained in previous stages of sampling ("adaptive" response surfaces). Here we consider progressive response surfaces where a balanced representation of global response over the parameter space is desired. We use Kriging and Moving-Least-Squares methods to fit Halton quasi-Monte-Carlo data samples and interpolate over the parameter space. On 2-D test problems we use the response surfaces to compute various response measures and assess the accuracy/applicability of heuristic error estimates based on convergence behavior of the computed response quantities. Where applicable we apply Richardson Extrapolation for estimates of error, and assess the accuracy of these estimates. We seek to develop a robust methodology for constructing progressive response surface approximations with reliable error estimates.

Swiler, Laura P.; Giunta, Anthony A.; Eldred, Michael S.; Adams, Brian M.; Romero, Vicente J.; Martinez-Canales, Monica L.

Abstract not provided.

Swiler, Laura P.; Trucano, Timothy G.; Oberkampf, William L.; Dowding, Kevin J.; Giunta, Anthony A.; Romero, Vicente J.; Romero, Vicente J.

Abstract not provided.

Romero, Vicente J.; Swiler, Laura P.

Abstract not provided.

Romero, Vicente J.; Romero, Vicente J.; Gunzburger, Max D.

A recently developed Centroidal Voronoi Tessellation (CVT) unstructured sampling method is investigated here to assess its suitability for use in statistical sampling and function integration. CVT efficiently generates a highly uniform distribution of sample points over arbitrarily shaped M-Dimensional parameter spaces. It has recently been shown on several 2-D test problems to provide superior point distributions for generating locally conforming response surfaces. In this paper, its performance as a statistical sampling and function integration method is compared to that of Latin-Hypercube Sampling (LHS) and Simple Random Sampling (SRS) Monte Carlo methods, and Halton and Hammersley quasi-Monte-Carlo sequence methods. Specifically, sampling efficiencies are compared for function integration and for resolving various statistics of response in a 2-D test problem. It is found that on balance CVT performs best of all these sampling methods on our test problems.

Trucano, Timothy G.; Easterling, Robert G.; Dowding, Kevin J.; Paez, Thomas L.; Urbina, Angel U.; Romero, Vicente J.; Rutherford, Brian M.; Hills, Richard G.

This report describes the underlying principles and goals of the Sandia ASCI Verification and Validation Program Validation Metrics Project. It also gives a technical description of two case studies, one in structural dynamics and the other in thermomechanics, that serve to focus the technical work of the project in Fiscal Year 2001.

Romero, Vicente J.; Swiler, Laura P.; Giunta, Anthony A.

This paper examines the modeling accuracy of finite element interpolation, kriging, and polynomial regression used in conjunction with the Progressive Lattice Sampling (PLS) incremental design-of-experiments approach. PLS is a paradigm for sampling a deterministic hypercubic parameter space by placing and incrementally adding samples in a manner intended to maximally reduce lack of knowledge in the parameter space. When combined with suitable interpolation methods, PLS is a formulation for progressive construction of response surface approximations (RSA) in which the RSA are efficiently upgradable, and upon upgrading, offer convergence information essential in estimating error introduced by the use of RSA in the problem. The three interpolation methods tried here are examined for performance in replicating an analytic test function as measured by several different indicators. The process described here provides a framework for future studies using other interpolation schemes, test functions, and measures of approximation quality.