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October 10, 2025Open Access

Longitudinal Flow Matching for Trajectory Modeling

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IMMFM captures stochastic dynamics using smooth interpolation paths, addressing limitations in existing models.
Experiments indicate that IMMFM yields improved forecasting accuracy on longitudinal neuroimaging datasets.
The approach employs joint optimization of drift and a data-driven diffusion coefficient to enhance model stability.
Results from synthetic benchmarks confirm the efficacy of IMMFM in handling irregular sparse sampling.

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The proposed framework improves forecasting accuracy in longitudinal neuroimaging, suggesting advancements in trajectory modeling.

Islam et al. (2025) studied this question.

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