Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems

Journal Article · Mon Jul 21 04:00:00 EDT 2025 · IEEE Computer Graphics and Applications

DOI:https://doi.org/10.1109/MCG.2025.3549665· OSTI ID:2588433

Potter, Kristi ^[1]; Molnar, Sam ^[1]; Laurence-Chasen, J.D. ^[1]; Duan, Yuhan ^[2]; Bessac, Julie ^[1]; Shen, Han-Wei ^[2]

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1. National Renewable Energy Lab., Golden, CO (United States)
2. The Ohio State University

Uncertainty visualization plays a critical role in transforming ensemble simulation data into actionable insights by effectively communicating various dimensions of uncertainty within a system. The emergence of artificial intelligence-driven surrogate models trained on multirun ensemble data offers a transformative opportunity to replace computationally intensive simulations with fast estimates, enabling users to explore data spaces with unprecedented depth and interactivity. However, integrating ensemble data and surrogate models into decision-making workflows and tools introduces novel challenges for uncertainty visualization. These include reconciling and clearly communicating the unique uncertainties associated with ensembles and their surrogate model estimates, and leveraging these approximations to inform actionable decisions. This work explores these challenges in the context of high-dimensional data visualization, bridging discrete datasets with their continuous representations and addressing the complexities of systems that support iterative navigation between input and output spaces. We evaluate the role of uncertainty visualization in fostering intuitive, actionable interactions and identify critical hurdles in advancing this frontier of computational simulation.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 2588433
• Report Number(s): NREL/JA-2C00-93009
• Journal Information: IEEE Computer Graphics and Applications, Journal Name: IEEE Computer Graphics and Applications Journal Issue: 3 Vol. 45
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
adaptation models
computational modeling
context modeling
data visualization
decision making
ensemble learning
high dimensional data
navigation
simulation
system performance
uncertainty
usability