Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models: Preprint

Conference · Thu Sep 12 04:00:00 EDT 2024

OSTI ID:2446840

Molnar, Sam; Laurence-Chasen, J. D.; Duan, Yuhan; Bessac, Julie; Potter, Kristi

Uncertainty visualization is a key component in translating important insights from ensemble data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models for computationally expensive simulations, users can interact with more aspects of data spaces than ever before. However, the integration of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new 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: 2446840
• Report Number(s): NREL/CP-2C00-90550; MainId:92328; UUID:e36c24df-13e2-41b9-9ce7-c12d04241ae9; MainAdminId:73691
• Country of Publication: United States
• Language: English

Related Subjects

MATHEMATICS AND COMPUTING
ensemble visualization
surrogate models
uncertainty
visualization