3 research outputs found
cellPACKexplorer: Interactive Model Building for Volumetric Data of Complex Cells
Given an algorithm the quality of the output largely depends on a proper
specification of the input parameters. A lot of work has been done to analyze
tasks related to using a fixed model [25] and finding a good set of inputs. In
this paper we present a different scenario, model building. In contrast to
model usage the underlying algorithm, i.e. the underlying model, changes and
therefore the associated parameters also change. Developing a new algorithm
requires a particular set of parameters that, on the one hand, give access to
an expected range of outputs and, on the other hand, are still interpretable.
As the model is developed and parameters are added, deleted, or changed
different features of the outputs are of interest. Therefore it is important to
find objective measures that quantify these features. In a model building
process these features are prone to change and need to be adaptable as the
model changes. We discuss these problems in the application of cellPACK, a tool
that generates virtual 3D cells. Our analysis is based on an output set
generated by sampling the input parameter space. Hence we also present
techniques and metrics to analyze an ensemble of probabilistic volumes
IsoTrotter: Visually Guided Empirical Modelling of Atmospheric Convection
Empirical models, fitted to data from observations, are often used in natural
sciences to describe physical behaviour and support discoveries. However, with
more complex models, the regression of parameters quickly becomes insufficient,
requiring a visual parameter space analysis to understand and optimize the
models. In this work, we present a design study for building a model describing
atmospheric convection. We present a mixed-initiative approach to visually
guided modelling, integrating an interactive visual parameter space analysis
with partial automatic parameter optimization. Our approach includes a new,
semi-automatic technique called IsoTrotting where we optimize the procedure by
navigating along isocontours of the model. We evaluate the model with unique
observational data of atmospheric convection based on flight trajectories of
paragliders.Comment: IEEEVIS 202
Pointwise Local Pattern Exploration for Sensitivity Analysis
Sensitivity analysis is a powerful method for discovering the significant factors that contribute to targets and understanding the interaction between variables in multivariate datasets. A number of sensitivity analysis methods fall into the class of local analysis, in which the sensitivity is defined as the partial derivatives of a target variable with respect to a group of independent variables. Incorporating sensitivity analysis in visual analytic tools is essential for multivariate phenomena analysis. However, most current multivariate visualization techniques do not allow users to explore local patterns individually for understanding the sensitivity from a pointwise view. In this paper, we present a novel pointwise local pattern exploration system for visual sensitivity analysis. Using this system, analysts are able to explore local patterns and the sensitivity at individual data points, which reveals the relationships between a focal point and its neighbors. During exploration, users are able to interactively change the derivative coefficients to perform sensitivity analysis based on different requirements as well as their domain knowledge. Each local pattern is assigned an outlier factor, so that users can quickly identify anomalous local patterns that do not conform with the global pattern. Users can also compare the local pattern with the global pattern both visually and statistically. Finally, the local pattern is integrated into the original attribute space using color mapping and jittering, which reveals the distribution of the partial derivatives. Case studies with real datasets are used to investigate the effectiveness of the visualizations and interactions