Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction

Wolfgang Berger, Harald Piringer, Peter Filzmoser, Meister Eduard Gröller
Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction
Computer Graphics Forum, 30(3):911-920, June 2011. [Paper]

Information

Abstract

Systems 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.

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BibTeX

@article{Berger_2011_UAE,
  title =      "Uncertainty-Aware Exploration of Continuous Parameter Spaces
               Using Multivariate Prediction",
  author =     "Wolfgang Berger and Harald Piringer and Peter Filzmoser and
               Meister Eduard Gr{"o}ller",
  year =       "2011",
  abstract =   "Systems 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.",
  month =      jun,
  issn =       "0167-7055",
  journal =    "Computer Graphics Forum",
  note =       "Best Paper Award",
  number =     "3",
  volume =     "30",
  pages =      "911--920",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2011/Berger_2011_UAE/",
}