Doctor of Philosophy, The Ohio State University, 2024, Computer Science and Engineering

Recent progress in high-performance computing now allows researchers to run extremely high-resolution computational models, simulating detailed physical phenomena. Yet, efficiently analyzing and visualizing the extensive data from these simulations is challenging. Adopting machine learning models to reduce the storage cost of or extract salient features from large scientific data has proven to be a successful approach to analyzing and visualizing these datasets effectively. Machine learning (ML) models like neural networks and Gaussian process models are powerful tools in data representation. They can capture the internal structures or ``features'' from the dataset, which is useful in compressing the data or exploring the subset of data that is of interest. However, applying machine learning models to scientific data brings new challenges to visualization. Machine learning models are usually computationally expensive. Neural networks are expensive to reconstruct on a dense grid representing a high-resolution scalar field and Gaussian processes are notorious for their cubic time complexity to the number of data points. If we consider other variables in the data modeling, for example, the time dimension and the simulation parameters in the ensemble data, the curse of dimensionality will make the computation cost even higher. The long inference time for the machine learning models puts us in a dilemma between the high data storage cost of the original data representation and the high computation cost of the machine learning representation. The above challenges demonstrate a great need for techniques and algorithms that increase the speed of ML model inference. Despite many generic efforts to increase ML efficiency, for example, using better hardware acceleration or designing more efficient architecture, we tackle a more specific problem of how to query the ML model more efficiently with a specific scientific visualization task. In this dissertation, we c (open full item for complete abstract)

Committee: Han-Wei Shen (Advisor); Hanqi Guo (Committee Member); Raphael Wenger (Committee Member) Subjects: Computer Engineering; Computer Science

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

Recent advancements in the field of computational sciences and high-performance computing have enabled scientists to design high-resolution computational models to simulate various real-world physical phenomenon. In order to gain key scientific insights about the underlying phenomena it is important to analyze and visualize the output data produced by such simulations. However, large-scale scientific simulations often produce output data whose size can range from a few hundred gigabytes to the scale of terabytes or even petabytes. Analyzing and visualizing such large-scale simulation data is not trivial. Moreover, scientific datasets are often multifaceted (multivariate, multi-run, multi-resolution, etc.), which can introduce additional complexities to the analyses and visualization activities. This dissertation addresses three broad categories of data analysis and visualization challenges: (i) multivariate distribution-based data summarization, (ii) uncertain analysis in ensemble simulation data, and (iii) simulation parameter analysis and exploration. We proposed statistical and machine learning-based approaches to overcome these challenges. A common strategy to deal with large-scale simulation data is to partition the simulation domain and create data summaries in the form of statistical probability distributions. Instead of storing high-resolution raw data, storing the compact statistical data summaries results in reduced storage overhead and alleviated I/O bottleneck issues. However, for multivariate simulation data using standard multivariate distributions for creating data summaries is not feasible. Therefore, we proposed a flexible copula-based multivariate distribution modeling strategy to create multivariate data summaries during simulation execution time (i.e, in-situ data modeling). The resulting data summaries can be subsequently used to perform scalable post-hoc analysis and visualization. In many cases, scientists execute their simulations mu (open full item for complete abstract)

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

PhD, University of Cincinnati, 2019, Arts and Sciences: Philosophy

Scientists often gain insight into real-world phenomena indirectly, through building and manipulating models. But what accounts for the epistemic import of model-based research? Why can scientists learn about real-world systems (such as the global climate or biological populations) by interacting not with the real-world systems themselves, but with computer simulations and mathematical equations? The traditional answer is that models teach us about certain real-world phenomena because they represent those phenomena. My dissertation challenges this representationalist intuition and provides an alternative framework for making sense of scientific modeling. The philosophical debate about scientific model-based representation has, by and large, proceeded in isolation from the debate about mental representation in philosophy of mind and cognitive science. Chapter one exposes and challenges this anti-psychologism. Drawing from "wide computationalist" embodied cognitive science research, I put forward an account of scientific models as socially-distributed and materially-extended mental representations. This account illustrates how views on mental representation can help advance philosophical understanding of scientific representation, while raising the question of how other views from (embodied) cognitive science might inform philosophical theorizing about scientific modeling. Chapter two argues that representationalism is untenable because it relies on ontological and epistemological assumptions that undermine one another no matter the theory of representation adopted. Views of scientific representation as mind-independent fail with the ontological claim that "models represent their targets" and thereby undermine the epistemological claim that "we learn from models because they represent their targets." On the other hand, views of scientific representation as mind-dependent support the ontological claim, but they do so in a way that also undermines the epistemolog (open full item for complete abstract)

Committee: Angela Potochnik Ph.D. (Committee Chair); Anthony Chemero Ph.D. (Committee Member); Thomas Polger Ph.D. (Committee Member); Michael Richardson Ph.D. (Committee Member) Subjects: Philosophy of Science