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Computational Science (computational + science)
Selected AbstractsA cyberenvironment for crystallography and materials science and an integrated user interface to the Crystallography Open Database and Predicted Crystallography Open DatabaseJOURNAL OF APPLIED CRYSTALLOGRAPHY, Issue 2 2008Jacob R. Fennick With the advent and subsequent evolution of the Internet the ways in which computational crystallographic research is conducted have dramatically changed. Consequently, secure, robust and efficient means of accessing remote data and computational resources have become a necessity. At present scientists in computational crystallography access remote data and resources via separate technologies, namely SSH and Web services. Computational Science and Engineering Online (CSE-Online) combines these two methods into a single seamless environment while simultaneously addressing issues such as stability with regard to Internet interruption. Presently CSE-Online contains several applications which are useful to crystallographers; however, continued development of new tools is necessary. Toward this end a Java application capable of running in CSE-Online, namely the Crystallography Open Database User Interface (CODUI), has been developed, which allows users to search for crystal structures stored in the Crystallography Open Database and Predicted Crystallography Open Database, to export structural data for visualization, or to input structural data in other CSE-Online applications. [source] Hamiltonian particle-mesh simulations for a non-hydrostatic vertical slice modelATMOSPHERIC SCIENCE LETTERS, Issue 4 2009Seoleun Shin Abstract A Lagrangian particle method is developed for the simulation of atmospheric flows in a non-hydrostatic vertical slice model. The proposed particle method is an extension of the Hamiltonian particle mesh (HPM) [Frank J, Gottwald G, Reich S. 2002. The Hamiltonian particle-mesh method. In Meshfree Methods for Partial Differential Equations, Lecture Notes in Computational Science and Engineering, Vol. 26, Griebel M, Schweitzer M (eds). Springer-Verlag: Berlin Heidelberg; 131,142] and provides preservation of mass, momentum, and energy. We tested the method for the gravity wave test in Skamarock W, Klemp J. 1994. Efficiency and accuracy of the Klemp-Wilhelmson time-splitting technique. Monthly Weather Review 122: 2623,2630 and the bubble experiments in Robert A. 1993. Bubble convection experiments with a semi-implicit formulation of the Euler equations. Journal of the Atmospheric Sciences 50: 1865,1873. The accuracy of the solutions from the HPM simulation is comparable to those reported in these references. A particularly appealing aspect of the method is in its non-diffusive transport of potential temperature. The solutions are maintained smooth largely due to a ,regularization' of pressure, which is controlled carefully to preserve the total energy and the time-reversibility of the model. In case of the bubble experiments, one also needs to regularize the buoyancy contributions. The simulations demonstrate that particle methods are potentially applicable to non-hydrostatic atmospheric flow regimes and that they lead to a highly accurate transport of materially conserved quantities such as potential temperature under adiabatic flow regimes. Copyright © 2009 Royal Meteorological Society [source] The Polder Computing Environment: a system for interactive distributed simulationCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 13-15 2002K. A. Iskra Abstract The paper provides an overview of an experimental, Grid-like computing environment, Polder, and its components. Polder offers high-performance computing and interactive simulation facilities to computational science. It was successfully implemented on a wide-area cluster system, the Distributed ASCI Supercomputer. An important issue is an efficient management of resources, in particular multi-level scheduling and migration of tasks that use PVM or sockets. The system can be applied to interactive simulation, where a cluster is used for high-performance computations, while a dedicated immersive interactive environment (CAVE) offers visualization and user interaction. Design considerations for the construction of dynamic exploration environments using such a system are discussed, in particular the use of intelligent agents for coordination. A case study of simulatedabdominal vascular reconstruction is subsequently presented: the results of computed tomography or magnetic resonance imaging of a patient are displayed in CAVE, and a surgeon can evaluate the possible treatments by performing the surgeries virtually and analysing the resulting blood flow which is simulated using the lattice-Boltzmann method. Copyright © 2002 John Wiley & Sons, Ltd. [source] Component-based, problem-solving environments for large-scale scientific computingCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 13-15 2002Chris Johnson Abstract In this paper we discuss three scientific computing problem solving environments: SCIRun, BioPSE, and Uintah. We begin with an overview of the systems, describe their underlying software architectures, discuss implementation issues, and give examples of their use in computational science and engineering applications. We conclude by discussing future research and development plans for the three problem solving environments. Copyright © 2002 John Wiley & Sons, Ltd. [source] Reliability of computational scienceNUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS, Issue 4 2007I. Babu Abstract Today's computers allow us to simulate large, complex physical problems. Many times the mathematical models describing such problems are based on a relatively small amount of available information such as experimental measurements. The question arises whether the computed data could be used as the basis for decision in critical engineering, economic, and medicine applications. The representative list of engineering accidents occurred in the past years and their reasons illustrate the question. The paper describes a general framework for verification and validation (V&V) which deals with this question. The framework is then applied to an illustrative engineering problem, in which the basis for decision is a specific quantity of interest, namely the probability that the quantity does not exceed a given value. The V&V framework is applied and explained in detail. The result of the analysis is the computation of the failure probability as well as a quantification of the confidence in the computation, depending on the amount of available experimental data. © 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 23: 753,784, 2007 [source] Applied Geometry:Discrete Differential Calculus for GraphicsCOMPUTER GRAPHICS FORUM, Issue 3 2004Mathieu Desbrun Geometry has been extensively studied for centuries, almost exclusively from a differential point of view. However, with the advent of the digital age, the interest directed to smooth surfaces has now partially shifted due to the growing importance of discrete geometry. From 3D surfaces in graphics to higher dimensional manifolds in mechanics, computational sciences must deal with sampled geometric data on a daily basis-hence our interest in Applied Geometry. In this talk we cover different aspects of Applied Geometry. First, we discuss the problem of Shape Approximation, where an initial surface is accurately discretized (i.e., remeshed) using anisotropic elements through error minimization. Second, once we have a discrete geometry to work with, we briefly show how to develop a full- blown discrete calculus on such discrete manifolds, allowing us to manipulate functions, vector fields, or even tensors while preserving the fundamental structures and invariants of the differential case. We will emphasize the applicability of our discrete variational approach to geometry by showing results on surface parameterization, smoothing, and remeshing, as well as virtual actors and thin-shell simulation. Joint work with: Pierre Alliez (INRIA), David Cohen-Steiner (Duke U.), Eitan Grinspun (NYU), Anil Hirani (Caltech), Jerrold E. Marsden (Caltech), Mark Meyer (Pixar), Fred Pighin (USC), Peter Schröder (Caltech), Yiying Tong (USC). [source] Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessmentENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 3 2010Gerald T. Ankley Abstract Ecological risk assessors face increasing demands to assess more chemicals, with greater speed and accuracy, and to do so using fewer resources and experimental animals. New approaches in biological and computational sciences may be able to generate mechanistic information that could help in meeting these challenges. However, to use mechanistic data to support chemical assessments, there is a need for effective translation of this information into endpoints meaningful to ecological risk,effects on survival, development, and reproduction in individual organisms and, by extension, impacts on populations. Here we discuss a framework designed for this purpose, the adverse outcome pathway (AOP). An AOP is a conceptual construct that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome at a biological level of organization relevant to risk assessment. The practical utility of AOPs for ecological risk assessment of chemicals is illustrated using five case examples. The examples demonstrate how the AOP concept can focus toxicity testing in terms of species and endpoint selection, enhance across-chemical extrapolation, and support prediction of mixture effects. The examples also show how AOPs facilitate use of molecular or biochemical endpoints (sometimes referred to as biomarkers) for forecasting chemical impacts on individuals and populations. In the concluding sections of the paper, we discuss how AOPs can help to guide research that supports chemical risk assessments and advocate for the incorporation of this approach into a broader systems biology framework. Environ. Toxicol. Chem. 2010;29:730,741. © 2009 SETAC [source] Complexity and the paradigm of Wolfram's A new kind of science: From the computational sciences to the science of computationCOMPLEXITY, Issue 4 2005Kovas Boguta First page of article [source] | |||||||||||||