A Survey of Representation Learning for Volumetric Scalar Fields

PhD Qualifying Examination


Title: "A Survey of Representation Learning for Volumetric Scalar Fields"

by

Mr. Bolin ZHAO


Abstract:

Volumetric scalar fields and their time-varying counterparts are widely 
produced across various scientific and engineering domains, such as physical 
oceanography, climate science, and manufacturing. Effectively representing 
this data is crucial for facilitating computation, storage, visualization, and 
analysis. In recent years, advances in learning-based methods have introduced 
a broad range of frameworks for representing scalar fields to support diverse 
tasks, such as generation, compression, and interactive visualization. This 
diversity of options necessitates a structured overview of their 
methodological foundations, supported tasks, and application settings. This 
survey provides a comprehensive and critical review of recent progress in 
learning-based representations for scalar fields. Following a structured 
literature collection and selection process, we organize the reviewed work 
into four major methodological categories: implicit neural representations, 
convolution-based methods, transformer-based methods, and Gaussian splatting. 
For each category, we examine how the corresponding approaches represent 
steady and unsteady scalar fields, the scientific tasks they support, and the 
settings in which they are applied. We further synthesize their technical 
characteristics, application objectives, and evaluation practices to present 
the relationships among different representation paradigms. Through this 
review, we aim to provide an accessible account of the development and current 
landscape of learning-based volumetric data representation, support the 
selection and comparison of representation approaches for different scientific 
tasks, and outline opportunities for continued research in this area.


Date:                   Tuesday, 29 September 2026

Time:                   9:00am - 11:00am

Venue:                  Room 5501
                        Lift 25/26

Committee Members:      Dr. Xiaojuan Ma (Supervisor)
                        Dr. Jun Han (Co-Supervisor)
                        Dr. Sehi L'Yi (Chairperson)
                        Dr. Arpit Narechania