Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction

dc.contributor.authorBerger, Wolfgangen_US
dc.contributor.authorPiringer, H.en_US
dc.contributor.authorFilzmoser, P.en_US
dc.contributor.authorGröller, Eduarden_US
dc.contributor.editorH. Hauser, H. Pfister, and J. J. van Wijken_US
dc.date.accessioned2014-02-21T20:23:36Z
dc.date.available2014-02-21T20:23:36Z
dc.date.issued2011en_US
dc.description.abstractSystems projecting a continuous n-dimensional parameter space to a continuous m-dimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this paper is an interactive approach to enable a continuous analysis of a sampled parameter space with respect to multiple target values. We employ methods from statistical learning to predict results in real-time at any user-defined point and its neighborhood. In particular, we describe techniques to guide the user to potentially interesting parameter regions, and we visualize the inherent uncertainty of predictions in 2D scatterplots and parallel coordinates. An evaluation describes a realworld scenario in the application context of car engine design and reports feedback of domain experts. The results indicate that our approach is suitable to accelerate a local sensitivity analysis of multiple target dimensions, and to determine a sufficient local sampling density for interesting parameter regions.en_US
dc.description.number3en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume30en_US
dc.identifier.doi10.1111/j.1467-8659.2011.01940.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01940.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectLine and curve generationen_US
dc.titleUncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Predictionen_US
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