Uncertainty Visualization Challenges in Decision Systems with Ensemble Data &amp; Surrogate Models

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

doi:10.1109/UncertaintyVisualization63963.2024.00006

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

Conference · Thu Nov 14 04:00:00 EST 2024

DOI:https://doi.org/10.1109/UncertaintyVisualization63963.2024.00006· OSTI ID:2497733

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

Uncertainty visualization is a key component in translating important insights from ensemble simulation data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models trained on ensemble data, we can substitute computationally expensive simulations, which allows users to interact with more aspects of data spaces than ever before. However, the use 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.

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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:: 2497733

Report Number(s):: NREL/CP-2C00-92628; MainId:94409; UUID:26911edf-720d-40b2-97cc-cfda08891afe; MainAdminId:75661

Country of Publication:: United States

Language:: English

References (29)

GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations Shi, Neng; Xu, Jiayi; Wurster, Skylar W. IEEE Transactions on Visualization and Computer Graphics https://doi.org/10.1109/TVCG.2022.3165345	journal	January 2022
Why Authors Don't Visualize Uncertainty Hullman, Jessica IEEE Transactions on Visualization and Computer Graphics, Vol. 26, Issue 1 https://doi.org/10.1109/TVCG.2019.2934287	journal	January 2020
Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations Padilla, Lace; Fygenson, Racquel; Castro, Spencer C. IEEE Transactions on Visualization and Computer Graphics https://doi.org/10.1109/TVCG.2022.3209457	journal	January 2022
Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data Potter, Kristin; Wilson, Andrew; Bremer, Peer-Timo 2009 IEEE International Conference on Data Mining Workshops (ICDMW) https://doi.org/10.1109/ICDMW.2009.55	conference	December 2009
Managing computational complexity using surrogate models: a critical review Alizadeh, Reza; Allen, Janet K.; Mistree, Farrokh Research in Engineering Design, Vol. 31, Issue 3 https://doi.org/10.1007/s00163-020-00336-7	journal	April 2020
Gaussian Processes for Machine Learning Seeger, Matthias International Journal of Neural Systems, Vol. 14, Issue 02 https://doi.org/10.1142/S0129065704001899	journal	April 2004
Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation Marrel, Amandine; Iooss, Bertrand Reliability Engineering & System Safety, Vol. 247 https://doi.org/10.1016/j.ress.2024.110094	journal	July 2024
Design by Dragging: An Interface for Creative Forward and Inverse Design with Simulation Ensembles Coffey, Dane; Erdman, Arthur G. IEEE Transactions on Visualization and Computer Graphics, Vol. 19, Issue 12 https://doi.org/10.1109/TVCG.2013.147	journal	December 2013
Approaches to uncertainty visualization Pang, Alex T.; Wittenbrink, Craig M.; Lodha, Suresh K. The Visual Computer, Vol. 13, Issue 8, p. 370-390 https://doi.org/10.1007/s003710050111	journal	November 1997
Overview and State-of-the-Art of Uncertainty Visualization Bonneau, Georges-Pierre; Hege, Hans-Christian; Johnson, Chris R. Mathematics and Visualization https://doi.org/10.1007/978-1-4471-6497-5_1	book	January 2014
Visual Parameter Space Analysis: A Conceptual Framework Sedlmair, Michael; Heinzl, Christoph; Bruckner, Stefan IEEE Transactions on Visualization and Computer Graphics, Vol. 20, Issue 12 https://doi.org/10.1109/TVCG.2014.2346321	journal	December 2014
A Next Step: Visualizing Errors and Uncertainty Johnson, C. R.; Sanderson, A. R. IEEE Computer Graphics and Applications, Vol. 23, Issue 5 https://doi.org/10.1109/MCG.2003.1231171	journal	September 2003
Recent advances and applications of surrogate models for finite element method computations: a review Kudela, Jakub; Matousek, Radomil Soft Computing, Vol. 26, Issue 24 https://doi.org/10.1007/s00500-022-07362-8	journal	July 2022
Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty Sanyal, J.; Dyer, J. IEEE Transactions on Visualization and Computer Graphics, Vol. 16, Issue 6 https://doi.org/10.1109/TVCG.2010.181	journal	November 2010
VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations Shi, Neng; Xu, Jiayi; Li, Haoyu IEEE Transactions on Visualization and Computer Graphics https://doi.org/10.1109/tvcg.2022.3209413	journal	January 2022
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial Nemani, Venkat; Biggio, Luca; Huan, Xun Mechanical Systems and Signal Processing, Vol. 205 https://doi.org/10.1016/j.ymssp.2023.110796	journal	December 2023
Visualization and Visual Analysis of Ensemble Data: A Survey Wang, Junpeng; Hazarika, Subhashis; Li, Cheng IEEE Transactions on Visualization and Computer Graphics, Vol. 25, Issue 9 https://doi.org/10.1109/TVCG.2018.2853721	journal	September 2019
Ensemble-Based Approaches Ensure Reliability and Reproducibility Wan, Shunzhou; Bhati, Agastya P.; Wade, Alexander D. Journal of Chemical Information and Modeling, Vol. 63, Issue 22 https://doi.org/10.1021/acs.jcim.3c01654	journal	November 2023
Parallelized Two-Dimensional Dam-Break Flood Analysis with Dynamic Data Structures Altinakar, Mustafa S.; McGrath, Marcus Z. World Environmental and Water Resources Congress 2012 https://doi.org/10.1061/9780784412312.151	conference	May 2012
A review of surrogate models and their application to groundwater modeling: SURROGATES OF GROUNDWATER MODELS Asher, M. J.; Croke, B. F. W.; Jakeman, A. J. Water Resources Research, Vol. 51, Issue 8 https://doi.org/10.1002/2015WR016967	journal	August 2015
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations He, Wenbin; Wang, Junpeng; Guo, Hanqi IEEE Transactions on Visualization and Computer Graphics https://doi.org/10.1109/TVCG.2019.2934312	journal	January 2019
Learning surrogate models for simulation-based optimization Cozad, Alison; Sahinidis, Nikolaos V.; Miller, David C. AIChE Journal, Vol. 60, Issue 6 https://doi.org/10.1002/aic.14418	journal	March 2014
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation Hazarika, Subhashis; Li, Haoyu; Wang, Ko-Chih IEEE Transactions on Visualization and Computer Graphics https://doi.org/10.1109/TVCG.2019.2934591	journal	January 2019
PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data Shen, Jingyi; Shen, Han-Wei IEEE Transactions on Visualization and Computer Graphics https://doi.org/10.1109/TVCG.2023.3327171	journal	January 2023
Result-Driven Exploration of Simulation Parameter Spaces for Visual Effects Design Bruckner, Stefan; Möller, Torsten IEEE Transactions on Visualization and Computer Graphics, Vol. 16, Issue 6 https://doi.org/10.1109/TVCG.2010.190	journal	November 2010
Dynamic parameter inverse analysis of concrete dams based on Jaya algorithm with Gaussian processes surrogate model Kang, Fei; Wu, Yingrui; Li, Junjie Advanced Engineering Informatics, Vol. 49 https://doi.org/10.1016/j.aei.2021.101348	journal	August 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Gal, Yarin; Ghahramani, Zoubin arXiv https://doi.org/10.48550/arXiv.1506.02142	preprint	January 2015
Introduction to Uncertainty Quantification Sullivan, T. J. Texts in Applied Mathematics https://doi.org/10.1007/978-3-319-23395-6	book	January 2015
From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches Potter, Kristin; Rosen, Paul; Johnson, Chris R. IFIP Advances in Information and Communication Technology https://doi.org/10.1007/978-3-642-32677-6_15	book	January 2012

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