Flow field exploration and visualization represent critical avenues of research for comprehending fluid phenomena.The continued advancement in computational power, particularly within high-performance computing systems, has enabled simulations of flow fields with increasingly higher resolutions. These advancements, while remarkable, bring forth new challenges that traditional post hoc exploration and visualization strategies struggle to address. Issues such as limited interactivity and poor visual perception become more pronounced as the complexity and scale of simulations increase. Furthermore, sophisticated visualization algorithms often make them too complex for implementation by scientists who may not have specialized expertise in computational visualization techniques. This scenario underscores the need for innovative approaches in the realm of post hoc exploration and visualization.Prioritizing advancements that bolster interactivity, enhance visual perception, and democratize access through easy-to-use, open-source tools is essential for the continued growth and applicability of flow field visualization.

The dissertation aims to address key challenges in flow field exploration and visualization, presenting new approaches to enhance the interactivity and visual perception of post hoc exploration and visualization. It also aims to provide an open-source solution to enable scientists to easily access and apply these advanced techniques. Initially, the dissertation introduces a deep-learning-based neural network for Lagrangian-based particle tracing.

As the first to employ deep learning in this context, it lays the groundwork for the proposed method through extensive experimentation. This includes evaluating two flow map extraction strategies and examining the effects of the number of training samples and integration durations on the method's effectiveness. The study further explores various sampling techniques for training and testing, alongside optimal hyperparameter configurations.

Building upon the initial study, the dissertation offers a comprehensive evaluation of the Lagrangian-based particle tracing neural network. This evaluation aims to establish a robust and efficient framework by assessing the model’s performance in accurately tracing particles across a range of settings, including 2D and 3D time-varying flow fields, flow fields from multiple applications, flow fields with varying complexities, as well as structured and unstructured input data. Additionally, it presents an empirical study to guide best practices in model architecture, activation functions, and training data structures. A comparative analysis of existing techniques using flow maps for visualization is also included. Furthermore, the dissertation explores the integration of the particle tracing model with various visualization interfaces, significantly enhancing interactivity. It introduces an interactive web-based interface and integrates high-fidelity visualization capabilities with an OSPRay-based viewer, leveraging the neural network's efficiency.

To address the need for high-performance and high-fidelity flow field rendering, the dissertation proposes a technique for ray tracing generalized tube primitives. This method, suitable for visualizing line-type data with variable radii, bifurcations, and accurate transparency, is implemented within the OSPRay open-source framework. It offers interactive, high-quality rendering capabilities with minimal memory overhead, marking a significant advancement in the field of flow visualization.