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Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction
| Citations: | 37 - 5 self |
BibTeX
@MISC{Berger_uncertainty-awareexploration,
author = {W. Berger and H. Piringer and P. Filzmoser and E. Gröller},
title = {Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction},
year = {}
}
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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. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation—Line and curve generation 1.
Keyphrases
uncertainty-aware exploration important role subject descriptor sample point sufficient local sampling density continuous m-dimensional target space inherent uncertainty interesting parameter region car engine design picture image generation line computer graphic main contribution parameter region continuous n-dimensional parameter space continuous analysis parallel coordinate realworld scenario domain expert multiple target dimension small number target value user-defined point acm cc sampled parameter space local sensitivity analysis report feedback interactive approach application context
