Information
- Publication Type: Journal Paper with Conference Talk
- Workgroup(s)/Project(s):
- Date: June 2011
- Journal: Computer Graphics Forum
- Volume: 30
- Number: 3
- Note: Best Paper Award
- Location: Bergen, Norway
- Lecturer: Wolfgang Berger
- ISSN: 0167-7055
- Event: EuroVis 2011
- Conference date: 31. May 2011 – 3. June 2011
- Pages: 911 – 920
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.Additional Files and Images
Weblinks
No further information available.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 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, journal = "Computer Graphics Forum", volume = "30", number = "3", note = "Best Paper Award", issn = "0167-7055", pages = "911--920", URL = "https://www.cg.tuwien.ac.at/research/publications/2011/Berger_2011_UAE/", }