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

Doctor of Philosophy, The Ohio State University, 2018, Computer Science and Engineering

Recent advancements in the computing power have enabled the application scientists to design their simulation study using very high-resolution computational models. The output data from such simulations provide a plethora of information that need to be explored for enhanced understanding of the underlying phenomena. Large-scale simulations, nowadays, produce multivariate, time-varying data sets in the order of petabytes and beyond. Traditional post-processing based analysis utilizing raw data cannot be readily applicable, since storing all the data is becoming prohibitively expensive. This is because of the bottleneck stemming from output data size and I/O compared to the ever-increasing computing speed. Hence, exploration and visualization of such extreme-scale simulation outputs are posing significant challenges. This dissertation addresses the aforementioned issues and suggests an alternative pathway by enabling in situ analysis, i.e., in-place analysis of data, while it still resides in supercomputer memory. We embrace the in situ technology and adopt simulation time data analysis, triage, and summarization using various data transformation techniques. The proposed methods process data as the simulation generates it and employ different analysis techniques to extract important data properties efficiently. However, the amount of work that can be done in situ is often limited in terms of time and storage since overburdening the simulation with additional computation is undesired. Furthermore, while some application domain driven analyses fit well for an in situ environment, a wide range of visual-analytics tasks require longer time involving iterative exploration during post-processing. Therefore, to this end, we conduct in situ statistical data summarization in the form of compact probability distribution functions, which preserve essential statistical data properties and facilitate flexible and scalable post-hoc exploration. We show that the reduced stati (open full item for complete abstract)

Committee: Han-Wei Shen (Advisor) Subjects: Computer Engineering; Computer Science