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VortexTransformer: End‐to‐End Objective Vortex Detection in 2D Unsteady Flow Using Transformers.

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Title: VortexTransformer: End‐to‐End Objective Vortex Detection in 2D Unsteady Flow Using Transformers.
Authors: Zhang, X.1 (AUTHOR), Rautek, P.1 (AUTHOR), Hadwiger, M.1 (AUTHOR)
Source: Computer Graphics Forum. May2025, Vol. 44 Issue 2, p1-15. 15p.
Subjects: Fluid dynamics, Vortex methods, Transformer models, Material point method, Deep learning
Abstract: Vortex structures play a pivotal role in understanding complex fluid dynamics, yet defining them rigorously remains challenging. One hard criterion is that a vortex detector must be objective, i.e., it needs to be indifferent to reference frame transformations. We propose VortexTransformer, a novel deep learning approach using point transformer architectures to directly extract vortex structures from pathlines. Unlike traditional methods that rely on grid‐based velocity fields in the Eulerian frame, our approach operates entirely on a Lagrangian representation of the flow field (i.e., pathlines), enabling objective identification of both strong and weak vortex structures. To train VortexTransformer, we generate a large synthetic dataset using parametric flow models to simulate diverse vortex configurations, ensuring a robust ground truth. We compare our method against CNN and U‐Net architectures, applying the trained models to real‐world flow datasets. VortexTransformer is an end‐to‐end detector, which means that reference frame transformations as well as vortex detection are handled implicitly by the network, demonstrating the ability to extract vortex boundaries without the need for parameters such as arbitrary thresholds, or an explicit definition of a vortex. Our method offers a new approach to determining objective vortex labels by using the objective pairwise distances of material points for vortex detection and is adaptable to various flow conditions. [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.)
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  Data: VortexTransformer: End‐to‐End Objective Vortex Detection in 2D Unsteady Flow Using Transformers.
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  Data: <searchLink fieldCode="DE" term="%22Fluid+dynamics%22">Fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Vortex+methods%22">Vortex methods</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Material+point+method%22">Material point method</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: Vortex structures play a pivotal role in understanding complex fluid dynamics, yet defining them rigorously remains challenging. One hard criterion is that a vortex detector must be objective, i.e., it needs to be indifferent to reference frame transformations. We propose VortexTransformer, a novel deep learning approach using point transformer architectures to directly extract vortex structures from pathlines. Unlike traditional methods that rely on grid‐based velocity fields in the Eulerian frame, our approach operates entirely on a Lagrangian representation of the flow field (i.e., pathlines), enabling objective identification of both strong and weak vortex structures. To train VortexTransformer, we generate a large synthetic dataset using parametric flow models to simulate diverse vortex configurations, ensuring a robust ground truth. We compare our method against CNN and U‐Net architectures, applying the trained models to real‐world flow datasets. VortexTransformer is an end‐to‐end detector, which means that reference frame transformations as well as vortex detection are handled implicitly by the network, demonstrating the ability to extract vortex boundaries without the need for parameters such as arbitrary thresholds, or an explicit definition of a vortex. Our method offers a new approach to determining objective vortex labels by using the objective pairwise distances of material points for vortex detection and is adaptable to various flow conditions. [ABSTRACT FROM AUTHOR]
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  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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        Value: 10.1111/cgf.70042
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      – Code: eng
        Text: English
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        PageCount: 15
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      – SubjectFull: Fluid dynamics
        Type: general
      – SubjectFull: Vortex methods
        Type: general
      – SubjectFull: Transformer models
        Type: general
      – SubjectFull: Material point method
        Type: general
      – SubjectFull: Deep learning
        Type: general
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      – TitleFull: VortexTransformer: End‐to‐End Objective Vortex Detection in 2D Unsteady Flow Using Transformers.
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            NameFull: Zhang, X.
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            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
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