Graphics4Science: Computer Graphics for Scientific Impacts

TL;DR

Graphics4Science explores the evolving role of computer graphics in scientific discovery, emphasizing its potential as a modeling language to address complex scientific challenges and foster interdisciplinary collaboration.

Contribution

The paper reframes computer graphics as a scientific modeling language, bridging gaps between graphics and science communities to enhance problem-solving in data-scarce scenarios.

Findings

Core methods like geometric reasoning aid scientific challenges.

Graphics as a modeling language bridges community gaps.

Potential for graphics to impact scientific discovery.

Abstract

Computer graphics, often associated with films, games, and visual effects, has long been a powerful tool for addressing scientific challenges--from its origins in 3D visualization for medical imaging to its role in modern computational modeling and simulation. This course explores the deep and evolving relationship between computer graphics and science, highlighting past achievements, ongoing contributions, and open questions that remain. We show how core methods, such as geometric reasoning and physical modeling, provide inductive biases that help address challenges in both fields, especially in data-scarce settings. To that end, we aim to reframe graphics as a modeling language for science by bridging vocabulary gaps between the two communities. Designed for both newcomers and experts, Graphics4Science invites the graphics community to engage with science, tackle high-impact problems where graphics expertise can make a difference, and contribute to the future of scientific discovery. Additional details are available on the course website: https://graphics4science.github.io