The volume of data produced by numerical simulations performed on high performance computers is becoming increasingly large. The visualization of these large post-generated volumes of data is currently a bottleneck fo...
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The formal language of Clifford’s algebras is attracting an increasingly large community of mathematicians, physicists and software developers seduced by the conciseness and the efficiency of this compelling system o...
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Many concepts in computational flow visualization operate in the Lagrangian frame—they involve the integration of trajectories. A problem inherent to these approaches is the choice of an appropriate time length for t...
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An ensemble is a group of related simulation runs, each consisting of the same set of variables, in a shared high-dimensional space describing a particular problem domain. Ensemble analysis looks at the combined behav...
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ISBN:
(纸本)9783662449004;9783662448991
An ensemble is a group of related simulation runs, each consisting of the same set of variables, in a shared high-dimensional space describing a particular problem domain. Ensemble analysis looks at the combined behaviors and features of the simulations to discover higher-level patterns that describe aspects of the underlying problem space. Sensitivity analysis is a type of ensemble analysis that evaluates how changes in simulation input parameters correlate with simulation results. Commonly, simple regression and multiple regression techniques are used to correlate single inputs to single outputs, or groups of inputs to a single output, respectively. However, neither of these approaches evaluates the collective relationships among multiple inputs and outputs. Existing visualization tools are fundamentally designed to view no more than a few simulations in combination. Ensembles containing hundreds or thousands of simulations require a different type of analysis, different visual abstractions, and a different system architecture to effectively manage integrating so many results. We present Slycat, a scalable, collaborative, remote analysis and visualization system designed for ensemble analysis. Slycat uses Canonical Correlation Analysis (CCA) to model relationships between input and output variables, providing a generalized correlation capability that analyzes any variable subsets from the two variable groups. Using linked views, we provide multiple representations of the CCA results for an ensemble, each showing the results at a different level of abstraction. The tight integration of analysis and visualization allows analysts to iteratively explore their data, forming and testing hypotheses about how simulation input parameters are driving output results in their ensembles. We provide two real-life examples using electrical circuit simulation ensembles of differing scales to demonstrate Slycat’s utility in answering common analysis questions. Slycat is available
The study of sublinear algorithms is a recent development in theoretical computer science and discrete mathematics that has significant potential to provide scalable analysis algorithms for massive data. The approache...
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Surface prototyping generates a so-called masterpiece of a new product. Usually, a masterpiece is very similar to the final product, so only small modifications are supposed to be made. We present a linear deformation...
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We present a new combinatorial algorithm for the optimal general topological simplification of scalar fields on surfaces. Given a piecewise linear (PL) scalar field f, our algorithm generates a simplified PL field g t...
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Regular maps are an algebraic concept to describe most symmetric tilings of closed surfaces of arbitrary genus. All such regular maps resp. symmetric tilings of surfaces up to genus 302 are algebraically known in the ...
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Topological analysis of multifields is an approaches to find meaningful, intrinsic structures in complex data. Methods introduced in previous years were usually evaluated separately or rather informally. However, to a...
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