Deep Learning for Scientific Visualization: Data Generation and Visualization Synthesis

Scientific simulations generate massive, time-varying volumetric data that pose significant challenges in I/O, storage, visualization, and analysis. This dissertation introduces deep learning–based frameworks to improve efficiency and flexibility in scientific visualization.

First, the Generalized Multivariate Translation (GMT) model enables scalable one-to-many and many-to-many variable translations, reducing training overhead while improving accuracy. Then, ViSNeRF leverages neural radiance fields for efficient, high-quality visualization synthesis from sparse images, supporting parameter-space exploration with multi-dimensional representation. Building on this, ReVolVE reconstructs volumetric scenes from rendered images, enabling visualization enhancement without access to raw volume data. A comprehensive study of neural surface reconstruction methods benchmarks their effectiveness on scientific datasets. Finally, VolSegGS introduces a Gaussian-splatting approach for real-time segmentation and tracking in dynamic volumetric scenes.

Together, these contributions advance deep learning–driven solutions for scientific visualization, offering faster, more flexible, and higher-fidelity analysis tools.