Doctor of Philosophy, The Ohio State University, 2016, Computer Science and Engineering

Analysis and quantification of uncertainty have become an integral part of the modern day data analysis and visualization frameworks. Varied amounts of uncertainty are introduced throughout the different stages of the visualization pipeline. While visualizing the scientific data sets, it is now imperative to provide an estimation of the associated uncertainty such that the users can readily assess the reliability of the visualization tools. Quantification of uncertainty is non-trivial for scalar data sets and this problem becomes even harder while handling multivariate and vector data sets. In this dissertation, several techniques are presented that identify, utilize and quantify uncertainty for multi-dimensional data sets. These techniques can be broadly classified into two groups: a) analysis of the existence of relationships and features and b) identification and analysis of error in flow visualization tools. The first category of studies use multivariate and ensemble datasets for analyzing relationship uncertainties. The second category of studies primarily use vector fields to demonstrate streamlines and stream surface for error analysis. In the analysis stage, we initially present an information theoretic framework towards the exploration of uncertainty in the relationships of multivariate datasets. We show that, in a multivariate system, variables can show interdependence on each other and information theoretic distance can be effectively used to find a hierarchical grouping of these variables. Using information content as the importance measure, salient variables are identified to start the variable exploration process. Specific mutual information is used for classifying the isosurfaces of one variable such that they reveal uncertainty regarding the other selected variables. Feedback from the ocean scientists establishes the superiority of this system over the existing techniques. From multivariate relationships, next we discuss the uncertainty in the rel (open full item for complete abstract)

Committee: Han-Wei Shen Dr. (Advisor); Raghu Machiraju Dr. (Committee Member); Huamin Wang Dr. (Committee Member) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy, The Ohio State University, 2019, Computer Science and Engineering

Ensemble simulations are becoming prevalent in various scientific and engineering disciplines, such as computational fluid dynamics, aerodynamics, climate, and weather research. Scientists routinely conduct a set of simulations with different configurations (e.g., initial/boundary conditions, parameter settings, or phenomenological models) and produce an ensemble of simulation outputs, namely an ensemble dataset. Ensemble datasets are extremely useful in studying the uncertainty of the simulation models and the sensitivities of the initial conditions and parameters. However, compared with deterministic scientific simulation data, visualizing and analyzing ensemble datasets are challenging because the ensemble datasets introduce extra dimensions into the field data (i.e., each spatial location is associated with multiple possible values instead of a deterministic value) and extra facets (e.g., simulation parameters). Over the last decade, various approaches have been proposed to visualize and analyze ensemble datasets from different perspectives. For example, the variability of isocontours is modeled and visualized by a collection of techniques. Coordinated multiple views are frequently used to visualize the simulation parameters and outputs simultaneously and linked together to study the influence of different simulation parameters. However, to handle different types of ensemble datasets (e.g., unstructured grid data, time-varying data, and extreme-scale data) and address various visualization tasks (e.g., uncertainty modeling and parameter space exploration), more work needs to be done in terms of ensemble data visualization and analysis. In this dissertation, we focus on visual exploration and analysis of ensemble datasets using statistical and deep learning models. Specifically, we explore and analyze ensemble datasets from three perspectives. First, we focus on modeling and visualizing the variability of ensemble members for 1) features (e.g., isosurfaces) (open full item for complete abstract)

Committee: Han-Wei Shen (Advisor); Rephael Wenger (Committee Member); Huamin Wang (Committee Member) Subjects: Computer Science

Master of Science, University of Toledo, 2019, Engineering (Computer Science)

The world is on the brink of an era of Unmanned Aerial Vehicles (UAVs), widely known to public as drones, where we will get to experience multiple UAVs flying in the national airspace carrying out diverse tasks such as monitoring, surveillance, product deliveries, law enforcement, fertilizing crop fields, aerial photography, and transport. In such scenarios, where multiple UAVs are flying in a smaller airspace, there is a possibility of collisions, path overlaps, mix-ups, and uncertainties as far as their flying routes are concerned. These flying routes could be inside constructed air corridors where the UAVs would be allotted to fly, similar to the air corridors of commercial aircraft. There is a growing need to identify and construct these air corridors for UAVs to fly in their respective corridors to avoid such mishaps as is what is done with commercial airplanes. The airplanes fly in their designated air corridors from one location to another without any uncertainty. It would be really useful to devise and design such a system for multiple UAVs as well, that would be able to construct air corridors for them to fly through. This served as the primary motivation behind proposing a novel approach to estimate and visualize air corridors for autonomous multi-UAV flights in an airspace. In addition to it, we studied various popular uncertainty visualization techniques and came up with a cutting-edge way to incorporate uncertainty into the visualization of the air corridors. Furthermore, we provide a standalone web application with a user-friendly graphical user interface (GUI) developed using HTML5, CSS3, JavaScript and an open-source JavaScript library for visualizing world-class 3-D maps called CesiumJS. Subsequently, we present the estimation and visualization results and discuss possible application areas where the proposed technique could be put to use. Finally, we discuss the summarized research findings and future research directions.

Committee: Ahmad Javaid (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Devinder Kaur (Committee Member) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy, The Ohio State University, 2016, Computer Science and Engineering

The rapid growth of computing power has expedited scientific simulations which can now generate data in unprecedentedly high quality and quantity. However, this advancement has not been mirrored in I/O performance, and hence scientific research is facing great challenges in visualizing and analyzing large-scale simulation results. Among areas of scientific research, fluid flow analysis plays an important role in many disciplines such as aerospace, climate modeling and medicine applications. The data-intensive computation required for fluid flow visualization makes it difficult to devise efficient algorithms and frameworks for flow analysis. First, to analyze a time-varying flow field, pathline visualization is typically used to reveal particle trajectories in the flow. Pathline computation, however, has irregular data access pattern that complicates out-of-core computation when the flow data are too large to fit in the main memory. Strategies on modeling the access pattern and improving spatial and temporal data locality are needed. Second, to avoid tremendous I/O latency, the simulated flow field results are typically down-sampled when they are stored, which inevitably affects the accuracy of the derived pathlines. Error reduction and modeling becomes important to enable uncertainty visualization in order for better decision making. This dissertation addresses the above challenges by data summarization approaches that efficiently process large data into succinct representations to facilitate flow analysis and visualization. First, a graph modeling approach is employed to encode the data access pattern of pathline computation, with which a cache-oblivious file layout algorithm and a work scheduling algorithm are proposed to optimize disk caching during out-of-core pathline visualization. Second, an incremental algorithm is devised that fits streaming time series of flow fields into higher-order polynomials and estimates errors in a compact distribution model. Th (open full item for complete abstract)

Committee: Han-Wei Shen (Advisor); Rephael Wenger (Committee Member); Jen-Ping Chen (Committee Member) Subjects: Computer Science