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Results for “Workflows”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Uncertainty quantification workflows in fusion plasma surface interaction modeling.
Abstract not provided.
Significant Advances in Computational Simulation Workflow for National Security Applications.
Abstract not provided.
Engineering Mod/Sim Workflow Platform and Shock Physics Exemplar.
Abstract not provided.
Sensitivity and Uncertainty Workflow of Full System SIERRA Models Supporting High Consequence Applications.
Abstract not provided.
Data Management and Scientific Workflows at Sandia National Labs.
Abstract not provided.
Manage Internal Queue w/ Flux for Common Workflows.
Abstract not provided.
Workflow Overviews and End-to-end Efficiency
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A Framework for End-to-End Computation Simulation Credibility Workflow.
Abstract not provided.
Credibility Framework ? an End-to-End Credibility Workflow Platform.
Abstract not provided.
Next Generation Workflow ? an Open Source Software System for Computational Modeling to Support Agile VVUQ.
Abstract not provided.
Workflows Containers Data Management and Scale Up Computing.
Abstract not provided.
The Role of Workflows in Credible High Consequence Computational Simulation.
Abstract not provided.
Visual HPC Workflows for the Analysis of System Dynamics Models
Visual analytics supported by high performance computing (HPC) accelerates and enhances the discovery, exploration, and analysis of causal patterns in complex system dynamics (SD) models. We present a suite of visualization-assisted ensemble-based techniques for hypothesis generation and testing, and for sensitivity analysis. By employing HPC to provide parallel, on-demand simulation of SD models, one can “steer” an ensemble of simulated scenarios in real time as one first formulates and then informally tests those hypotheses: this provides rapid feedback for analysts to refine their understanding of the causal relationships emergent from a model. Such understandings can be followed and augmented by rigorous application of statistical methods, namely global variance-based sensitivity analysis, Monte-Carlo filtering, adaptive regional sensitivity analysis, and self-organized maps: here timely computation relies on HPC, while effective presentation emphasizes high-dimensional multivariate data visualization. Immersive visualization in virtual 3D environments provides an excellent adjunct to the traditional 2D graphics typically used for SD models, as it generates an embodied understanding of model behavior and facilitates an active, collaborative critique of model structure and output. Finally, we summarize prospects for HPC-enabled visual analytics applied to SD modeling.