|
Award Number and Duration |
NSF IIS 2145499 |
PI and Point of Contact |
Bei Wang |
Overview |
Data generated from multiphysics simulations, such as binary black hole mergers and fluid dynamics, have experienced exponential growth because of the growing capabilities of computing facilities. At the same time, data-intensive science relies on the acquisition, management, analysis, and visualization of data with increasing spatial and temporal resolutions. This project develops a new set of approaches to support the core tasks in scientific data visualization (such as feature tracking, event detection, ensemble analysis, and interactive visualization) in a way that is more reflective of the underlying physics using measure theory. The results will be instantiated by a collection of open-source software tools to be deployed for the collaborating scientists in materials science and high-performance computing, and the larger research community. |
Broader Impacts |
This project provides a unique environment for multidisciplinary activities and training opportunities for undergraduate and graduate students. The research will be used to enhance course materials in computational topology, data visualization, and scientific computing, and more importantly, to promote visualization as a core part of training in data science. The PI will improve the impact and accessibility of education via her public YouTube channels. The project results will help the students discover how new and advanced data visualization tools offer analytics capabilities for large and complex data. This project will have a large impact on application domains via multidisciplinary collaborations in materials sciences and high performance computing. |
Publications and Manuscripts |
Year 1 (2022 - 2023) | |
Meta-diagrams for 2-parameter persistence. Nate Clause, Tamal K. Dey, Facundo Mémoli, Bei Wang. International Symposium on Computational Geometry (SOCG), 2023. | |
TopoSZ: Preserving Topology in Error-Bounded Lossy Compression.
Lin Yan, Xin Liang, Hanqi Guo, Bei Wang. IEEE Visualization Conference, conditional accepted, 2023. arXiv:2304.11768. | |
TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclone.
Lin Yan, Hanqi Guo, Tom Peterka, Bei Wang, Jiali Wang. IEEE Visualization Conference, conditional accepted, 2023. | |
Flexible and Probabilistic Topology Tracking with Partial Optimal Transport.
Mingzhe Li, Xinyuan Yan, Lin Yan, Tom Needham, Bei Wang. Manuscript, 2023. arXiv:2302.02895. | |
Hypergraph Co-Optimal Transport: Metric and Categorical Properties.
Samir Chowdhury, Tom Needham, Ethan Semrad, Bei Wang, Youjia Zhou. Manuscript, 2023. arXiv:2112.03904. | |
Comparing Morse Complexes Using Optimal Transport:
An Experimental Study.
Carson Storm, Mingzhe Li, Austin Yang Li, Tom Needham, Bei Wang. Manuscript, 2023. | |
Labeled Interleaving Distance for Reeb Graphs.
Fangfei Lan, Salman Parsa, Bei Wang. Manuscript, 2023. arXiv:2306.01186 | |
Sketching and Vectorizing Merge Trees for Scientific Data Visualization.
Mingzhe Li, Sourabh Palande, Lin Yan, Bei Wang. Manuscript, 2023. arXiv:2101.03196. | |
Multilevel Robustness for 2D Vector Field Feature Tracking, Selection, and Comparison. Lin Yan, Paul Aaron Ullrich, Luke P. Van Roekel, Bei Wang, Hanqi Guo. Computer Graphics Forum, 2023. DOI: 10.1111/cgf.14799 arXiv:2209.11708 | |
Uncertainty Visualization for Graph Coarsening. Fangfei Lan, Sourabh Palande, Michael Young, Bei Wang. IEEE International Conference on Big Data (IEEE BigData), pages 2922-2931, 2022. DOI: 10.1109/BigData55660.2022.10021039 |
Presentations, Educational Development and Broader Impacts |
Year 1 (2022 - 2023) |
|
Collaborators |
Dr. Lin Yan, Environmental Science & Mathematics and Computer Science, Argonne National Laboratory, Lemont, USA Dr. Paul Aaron Ullrich, Department of Land, Air and Water Resources, University of California, Davis, USA Dr. Luke P. Van Roekel, Fluid Dynamics and Solid Mechanics, Los Alamos National Laboratory Los Alamos, USA Dr. Hanqi Guo, Department of Computer Science and Engineering, The Ohio State University, Columbus, USA |
Acknowledgement |
This material is based upon work supported or partially supported by the National Science Foundation under Grant No. 2145499. |