A Multiplexing Design Space: Theory, Method, and Application

2026-06-10

AI Summary

Existing visualization techniques lack a systematic approach to visual redundancy when simultaneously representing multiple correlated 2D scalar fields, and conventional methods such as overlaid heatmaps offer limited effectiveness. This work proposes a domain-driven visual redundancy design methodology that, for the first time, formulates visual redundancy as a compact, systematically explorable design space. By integrating visualization theory, human–computer collaborative workflows, and pre-analysis, the approach yields a default near-optimal solution tailored for multi-scalar-field visualization while supporting expert customization. The method has been successfully deployed in machine learning–based partial differential equation solvers, and expert evaluations confirm its efficacy in clearly and efficiently communicating complex inter-field relationships.

Abstract

Many visualization designs feature phenomena referred to as ``visual multiplexing'', where multiple pieces of information associated with the same data point are conveyed simultaneously. Although visualization designers are able to bring such phenomena, often unconsciously, into their designs, the design space of visual multiplexing is huge, and it is uncommon to explore visual multiplexing systematically as design patterns. In this paper, we propose a design method for exploring a smaller design space constrained by an application. As an illustrative case study, we focus on machine learning (ML) workflows for developing ML models that approximate partial differential equations (PDEs). In these workflows, ML researchers need to analyze the inter-relationships among multiple 2D scalar fields frequently. Since superimposing one heatmap on top of another is not an effective design, we formulate three design steps to explore the design space of visual multiplexing in the context of multiple 2D scalar fields. Our design method also includes a pre-design step for domain grounding and theoretical analysis, and involves domain experts in both co-design and evaluation activities. The design process enables us to identify relatively optimal default multiplexing designs as well as the need for small variations that domain experts can control through a user interface.

Problem

• visual multiplexing
• design space
• 2D scalar fields
• machine learning workflows
• visualization design

Innovation

• visual multiplexing
• design space
• co-design
• scalar field visualization
• machine learning workflows