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Leveraging deep learning-based foundation models for optical turbulence (Cn2) estimation under data scarcity.

Sukanta Basu

    Applied Optics
    |March 17, 2026
    PubMed
    Summary

    Tabular foundation models (TFMs) can predict optical turbulence (Cn2) using limited data. These models, pre-trained on diverse datasets, offer a fast, tuning-free solution for optical turbulence prediction.

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    Area of Science:

    • Atmospheric physics and remote sensing
    • Machine learning applications in geosciences
    • Optical engineering and adaptive optics

    Background:

    • Estimating optical turbulence (Cn2) is crucial for applications like astronomy and free-space communication.
    • Data-driven Cn2 estimation is hindered by sparse and costly high-quality training data.
    • Existing methods often require extensive data or complex physics-based models.

    Purpose of the Study:

    • To investigate the efficacy of tabular foundation models (TFMs) for data-driven optical turbulence (Cn2) estimation.
    • To assess TFMs' performance in a few-shot learning scenario with minimal task-specific adaptation.
    • To determine if TFMs pre-trained on general tabular data can generalize to the specialized domain of optical turbulence.

    Main Methods:

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    • Utilized meteorological and turbulence observations from the Mauna Loa Observatory.
    • Evaluated two state-of-the-art TFMs: TabPFNv2 and TabDPT.
    • Employed a few-shot learning approach without fine-tuning or hyperparameter optimization.

    Main Results:

    • Both TFMs accurately captured the diurnal cycle and dynamic range of Cn2.
    • TabPFNv2 demonstrated superior data efficiency and ensemble stability.
    • Model performance matched or exceeded previous data-driven and physics-based approaches.
    • Feature importance analysis revealed physically meaningful learned relationships in TabPFNv2, aligning with surface layer theory.

    Conclusions:

    • TFMs show significant promise for optical turbulence prediction, especially in data-limited scenarios.
    • TFMs offer a "plug-and-play" solution, reducing the need for extensive data and hyperparameter tuning.
    • These findings pave the way for faster, more accessible operational optical turbulence forecasting.