HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization

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dc.contributor.authorGadirov, Hamiden_US
dc.contributor.authorWu, Qien_US
dc.contributor.authorBauer, Daviden_US
dc.contributor.authorMa, Kwan-Liuen_US
dc.contributor.authorRoerdink, Jos B.T.M.en_US
dc.contributor.authorFrey, Steffenen_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorAndrienko, Nataliaen_US
dc.contributor.editorWang, Beien_US
dc.date.accessioned2025-05-26T06:38:38Z
dc.date.available2025-05-26T06:38:38Z
dc.date.issued2025
dc.description.abstractWe present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data. This work addresses the critical need to explicitly incorporate ensemble parameters into the learning process, as traditional methods often neglect these, limiting their ability to adapt to diverse simulation settings and provide meaningful insights into the data dynamics. HyperFLINT introduces a hypernetwork to account for simulation parameters, enabling it to generate accurate interpolations and flow fields for each timestep by dynamically adapting to varying conditions, thereby outperforming existing parameter-agnostic approaches. The architecture features modular neural blocks with convolutional and deconvolutional layers, supported by a hypernetwork that generates weights for the main network, allowing the model to better capture intricate simulation dynamics. A series of experiments demonstrates HyperFLINT's significantly improved performance in flow field estimation and temporal interpolation, as well as its potential in enabling parameter space exploration, offering valuable insights into complex scientific ensembles.en_US
dc.description.sectionheadersFlow Vis
dc.description.seriesinformationComputer Graphics Forum
dc.identifier.doi10.1111/cgf.70134
dc.identifier.issn1467-8659
dc.identifier.pages15 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70134
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70134
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Flow Estimation; Interpolation; Deep Learning; Human-centered computing → Spatiotemporal Data; Ensemble Parameter Space Exploration; Scientific visualization
dc.subjectComputing methodologies → Flow Estimation
dc.subjectInterpolation
dc.subjectDeep Learning
dc.subjectHuman centered computing → Spatiotemporal Data
dc.subjectEnsemble Parameter Space Exploration
dc.subjectScientific visualization
dc.titleHyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualizationen_US
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