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Optimal Transport‐Based Data Assimilation Scheme for Individual‐Based Models.

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Title: Optimal Transport‐Based Data Assimilation Scheme for Individual‐Based Models.
Authors: Alver, M. O.1 (AUTHOR) morten.alver@ntnu.no, Kelly, C.2 (AUTHOR), Talebi, Sayed Pouria (AUTHOR) s.talebi12@alumni.imperial.ac.uk
Source: Journal of Applied Mathematics. 6/11/2026, Vol. 2026, p1-15. 15p.
Subjects: Data assimilation, Mathematical optimization, Microsimulation modeling (Statistics), Animal population density, Kalman filtering
Abstract: In this study, a data assimilation scheme for individual‐based models (IBMs) based on optimal transport theory is defined and analyzed. When simulating an IBM and acquiring observations of population densities and food concentrations, the assimilation scheme allows the observations to be used for model correction by deriving density fields, performing an ensemble Kalman filter update on the derived fields, and then updating the IBM. The update can be done as a full resampling of the IBM, but this breaks the continuity of individual trajectories. In this study, optimal transport theory is used to design an alternative update method. The algorithm computes transport plans for moving individuals to achieve the target density fields while optimizing transport costs computed as a function of individual movement distances. The performance of the method using the two alternative approaches was analyzed for a synthetic test system. The update method based on optimal transport achieved similar accuracy as a method based on resampling the IBM while also preserving coherent individual trajectories, with the trade‐off being a higher computational cost. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Mathematics 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: <searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Microsimulation+modeling+%28Statistics%29%22">Microsimulation modeling (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Animal+population+density%22">Animal population density</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink>
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  Data: In this study, a data assimilation scheme for individual‐based models (IBMs) based on optimal transport theory is defined and analyzed. When simulating an IBM and acquiring observations of population densities and food concentrations, the assimilation scheme allows the observations to be used for model correction by deriving density fields, performing an ensemble Kalman filter update on the derived fields, and then updating the IBM. The update can be done as a full resampling of the IBM, but this breaks the continuity of individual trajectories. In this study, optimal transport theory is used to design an alternative update method. The algorithm computes transport plans for moving individuals to achieve the target density fields while optimizing transport costs computed as a function of individual movement distances. The performance of the method using the two alternative approaches was analyzed for a synthetic test system. The update method based on optimal transport achieved similar accuracy as a method based on resampling the IBM while also preserving coherent individual trajectories, with the trade‐off being a higher computational cost. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Applied Mathematics 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: 10.1155/jama/6739019
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      – Code: eng
        Text: English
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        PageCount: 15
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      – SubjectFull: Data assimilation
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Microsimulation modeling (Statistics)
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      – SubjectFull: Animal population density
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      – SubjectFull: Kalman filtering
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      – TitleFull: Optimal Transport‐Based Data Assimilation Scheme for Individual‐Based Models.
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              M: 06
              Text: 6/11/2026
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              Y: 2026
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