Soumya Dutta

An Information-Aware Framework for Exploring Multivariate Data Sets

Information theory provides a theoretical framework for measuring information content for an observed variable, and has attracted much attention from visualization researchers for its ability to quantify saliency and similarity among variables. In this paper, we present a new approach towards building an exploration framework based on information theory to guide the users through the multivariate data exploration process. In our framework, we compute the total entropy of the multivariate data set and identify the contribution of individual variables to the total entropy. The variables are classified into groups based on a novel graph model where a node represents a variable and the links encode the mutual information shared between the variables. The variables inside the groups are analyzed for their representativeness and an information based importance is assigned. We exploit specific information metrics to analyze the relationship between the variables and use the metrics to choose isocontours of selected variables. For a chosen group of points, parallel coordinates plots (PCP) are used to show the states of the variables and provide an interface for the user to select values of interest. Experiments with different data sets reveal the effectiveness of our proposed framework in depicting the interesting regions of the data sets taking into account the interaction among the variables.

Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis

Effective analysis of features in time-varying data is essential in numerous scientific applications. Feature extraction and tracking are two important tasks scientists rely upon to get insights about the dynamic nature of the large scale time-varying data. However, often the complexity of the scientific phenomena only allows scientists to vaguely define their feature of interest. Furthermore, such features can have varying motion patterns and dynamic evolution over time. As a result, automatic extraction and tracking of features becomes a non-trivial task. In this work, we investigate these issues and propose a distribution driven approach which allows us to construct novel algorithms for reliable feature extraction and tracking with high confidence in the absence of accurate feature definition. We exploit two key properties of an object, motion and similarity to the target feature, and fuse the information gained from them to generate a robust feature-aware classification field at every time step. Tracking of features is done using such classified fields which enhances the accuracy and robustness of the proposed algorithm. The efficacy of our method is demonstrated by successfully applying it on several scientific data sets containing a wide range of dynamic time-varying features.

In Situ Distribution Guided Analysis and Visualization of Transonic Jet Engine Simulations

Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the understanding of flow phenomena undergoing rotating stall. Despite the proven high modeling accuracy of TURBO, the excessive simulation data prohibits post-hoc analysis in both storage and I/O time. To address these issues and allow the expert to perform scalable stall analysis, we have designed an in situ distribution guided stall analysis technique. Our method summarizes statistics of important properties of the simulation data in situ using a probabilistic data modeling scheme. This data summarization enables statistical anomaly detection for flow instability in post analysis, which reveals the spatiotemporal trends of rotating stall for the expert to conceive new hypotheses. Furthermore, the verification of the hypotheses and exploratory visualization using the summarized data are realized using probabilistic visualization techniques such as uncertain isocontouring. Positive feedback from the domain scientist has indicated the efficacy of our system in exploratory stall analysis.

Visualization and Analysis of Rotating Stall for Transonic Jet Engine Simulation

Identification of early signs of rotating stall is essential for the study of turbine engine stability. With recent advancements of high performance computing, high-resolution unsteady flow fields allow in depth exploration of rotating stall and its possible causes. Performing stall analysis, however, involves significant effort to process large amounts of simulation data, especially when investigating abnormalities across many time steps. In order to assist scientists during the exploration process, we present a visual analytics framework to identify suspected spatiotemporal regions through a comparative visualization so that scientists are able to focus on relevant data in more detail. To achieve this, we propose efficient stall analysis algorithms derived from domain knowledge and convey the analysis results through juxtaposed interactive plots. Using our integrated visualization system, scientists can visually investigate the detected regions for potential stall initiation and further explore these regions to enhance the understanding of this phenomenon. Positive feedback from scientists demonstrate the efficacy of our system in analyzing rotating stall.

Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets

Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments.

Pointwise information guided visual analysis of time-varying multi-fields

Identification of salient features from a time-varying multivariate system plays an important role in scientific data understanding. In this work, we present a unified analysis framework based on mutual information and two of its decomposition: specific and pointwise mutual information to quantify the amount of information content between different value combinations from multiple variables over time. The pointwise mutual information (PMI), computed for each value combination, is used to construct informative scalar fields, which allow close examination of combined and complementary information possessed by multiple variables. Since PMI gives us a way of quantifying information shared among all combinations of scalar values for multiple variables, it is used to identify salient isovalue tuples. Visualization of isosurfaces on those selected tuples depicts combined or complementary relationships in the data. For intuitive interaction with the data, an interactive interface is designed based on the proposed information-theoretic measures. Finally, successful application of the proposed method on two time-varying data sets demonstrates the efficacy of the system.

Visualizing the variations of ensemble of isosurfaces

Visualizing the similarities and differences among an ensemble of isosurfaces is a challenging problem mainly because the isosurfaces cannot be displayed together at the same time. For ensemble of isosurfaces, visualizing these spatial differences among the surfaces is essential to get useful insights as to how the individual ensemble simulations affect different isosurfaces. We propose a scheme to visualize the spatial variations of isosurfaces with respect to statistically significant isosurfaces within the ensemble. Understanding such variations among ensemble of isosurfaces at different spatial regions is helpful in analyzing the influence of different ensemble runs over the spatial domain. In this regard, we propose an isosurface-entropy based clustering scheme to divide the spatial domain into regions of high and low isosurface variation. We demonstrate the efficacy of our method by successfully applying it on real-world ensemble data sets from ocean simulation experiments and weather forecasts.

OpenMC In Situ Source Convergence Detection

We designed and implemented an in situ version of particle source convergence for the OpenMC particle transport simulator. OpenMC is a Monte Carlo based-particle simulator for neutron criticality calculations. For the transport simulation to be accurate, source particles must converge on a spatial distribution. Typically, convergence is obtained by iterating the simulation by a user-settable, fixed number of steps, and it is assumed that convergence is achieved. We instead implement a method to detect convergence, using the stochastic oscillator for identifying convergence of source particles based on their accumulated Shannon Entropy. Using our in situ convergence detection, we are able to detect and begin tallying results for the full simulation once the proper source distribution has been confirmed. Our method ensures that the simulation is not started too early, by a user setting too optimistic parameters, or too late, by setting too conservative a parameter.