HyperFLINT: Hypernetwork‐based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization.
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| Title: | HyperFLINT: Hypernetwork‐based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization. |
|---|---|
| Authors: | Gadirov, Hamid1 (AUTHOR), Wu, Qi2 (AUTHOR), Bauer, David3 (AUTHOR), Ma, Kwan‐Liu3 (AUTHOR), Roerdink, Jos B.T.M.1 (AUTHOR), Frey, Steffen1 (AUTHOR) |
| Source: | Computer Graphics Forum. Jun2025, Vol. 44 Issue 3, p1-15. 15p. |
| Subjects: | Scientific visualization, Interpolation, Statistical ensembles, Deep learning, Flow measurement, Spatiotemporal processes |
| Abstract: | We 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186836980 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: HyperFLINT: Hypernetwork‐based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gadirov%2C+Hamid%22">Gadirov, Hamid</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Qi%22">Wu, Qi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bauer%2C+David%22">Bauer, David</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Kwan‐Liu%22">Ma, Kwan‐Liu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Roerdink%2C+Jos+B%2ET%2EM%2E%22">Roerdink, Jos B.T.M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Frey%2C+Steffen%22">Frey, Steffen</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Graphics+Forum%22">Computer Graphics Forum</searchLink>. Jun2025, Vol. 44 Issue 3, p1-15. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Scientific+visualization%22">Scientific visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Interpolation%22">Interpolation</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+ensembles%22">Statistical ensembles</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Flow+measurement%22">Flow measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/cgf.70134 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Scientific visualization Type: general – SubjectFull: Interpolation Type: general – SubjectFull: Statistical ensembles Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Flow measurement Type: general – SubjectFull: Spatiotemporal processes Type: general Titles: – TitleFull: HyperFLINT: Hypernetwork‐based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gadirov, Hamid – PersonEntity: Name: NameFull: Wu, Qi – PersonEntity: Name: NameFull: Bauer, David – PersonEntity: Name: NameFull: Ma, Kwan‐Liu – PersonEntity: Name: NameFull: Roerdink, Jos B.T.M. – PersonEntity: Name: NameFull: Frey, Steffen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 01677055 Numbering: – Type: volume Value: 44 – Type: issue Value: 3 Titles: – TitleFull: Computer Graphics Forum Type: main |
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