trucano timothy (3 Ergebnisse)

Wraps. Presumed first edition/first printing. 77, [3] p. Includes illustrations. Some illustrations in color. References. This report described the underlying principles and goals of the sandia Accelerated Strategic Computing Initiative (ASCI) Verification and Validation Program Validation Metrics Project. It also gives a technical description fo two case studies, one in structural dynamics and the aother in theromomechanics, that serve to focus the technical work of the prject in Fiscal Year 2001. Good. No dust jacket. Staple bound.

Spiralbound. Presumed first edition/first printing. 58, [2] p. Includes illustrations. Acronyms. References. List of Figures. List of Tables. This reprot describes the results of the FY01 Level 1 Peer Reviews for the Verification and Validation (V&V) Program at Sandia National Laboratories. The peer review was intended to assess the ASCI code team V&V planning process and execution. The peer review process was conducted in accordance with the guidelines defined in SAND2000-3099. V&V Plans were developed in accordance with the guidelines defined in SAND2000-3101. The peer review process and the process for improving the Guidelines are necessarily synchronized and from parts of a larger quality improvement process supporting the ASCI program at Sandia. In FY01 Peer Reviews were conducted on eleven code teams and their respective V&V plans. This report summarizes the results from those peer reviews, including recommendations form the panels that conducted the reviews. Very good. No dust jacket. Cover has slight wear and soiling.

Spiral bound. Zustand: Good. Presumed First Edition, First printing. 86, [2] pages. Tables. Figures. References. Minor page rippling at bottom edge. Two major issues associated with model validation are addressed here. First, we present a maximum likelihood approach to define and evaluate a model validation metric. The advantage of this approach is it is more easily applied to nonlinear problems than the methods presented earlier by Hills and Trucano (1999, 2001); the method is based on optimization for which software packages are readily available; and the method can more easily be extended to handle measurement uncertainty and prediction uncertainty with different probability structures. Several examples are presented utilizing this metric. We show conditions under which this approach reduces to the approach developed previously by Hills and Trucano (2001). Secondly, we expand our earlier discussions (Hills and Trucano, 1999, 2001) on the impact of multivariate correlation and the effect of this on model validation metrics. We show that ignoring correlation in multivariate data can lead to misleading results, such as rejecting a good model when sufficient evidence to do so is not available.