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        <h1 class="px-4 py-2 fs-large border-start border-2 border-white paint-large-header">Papers</h1>
        <p class="my-3 dynamic-p paint-small-header">Research on the basis of PAINT</p>
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<h4 class="my-4 py-2 text-primary">The PAINT Paper</h4>

The PAINT database is described in detail in Nature Energy:

<ul>
    <li>K. Phipps, M. Kuhl, M. Weiel, M. Busch, J. Lewen, N. Blumenröhr, D. Maldonado Quinto, C. Debus, F. Göhring, O. Kaufhold, A. Streit, R. Pitz-Paal, M. Götz, M. Pargmann "The PAINT database for operational concentrating solar power plant data following FAIR data principles" <i>Nature Energy</i> (2026), doi: <a href="https://doi.org/10.1038/s41560-026-02070-1">10.1038/s41560-026-02070-1</a>.</li>
</ul>

<h4 class="my-4 py-2 text-primary">Additional papers based on PAINT</h4>

<ul>
    <li>M. Pargmann, J. Ebert, M. Götz, D.M. Quinto, R. Pitz-Paal, S. Kesselheim "Automatic Heliostat Learning for In-situ Concentrating Solar Power Plant Metrology with Differentiable Ray Tracing" <i>Nature Communications</i> 15(1) (2024): 6997, doi: <a href="https://doi.org/10.1038/s41467-024-51019-z">10.1038/s41467-024-51019-z</a>.</li>
    <li>M. Kuhl, M. Pargmann, M. Cherti, J. Jitsev, D.M. Quinto, R. Pitz-Paal "Flux density distribution forecasting in concentrated solar tower plants: A data-driven approach" <i>Solar Energy</i> 282 (2024): 112894, doi: <a href="https://doi.org/10.1016/j.solener.2024.112894">10.1016/j.solener.2024.112894</a>.</li>
    <li>M. Pargmann, D.M. Quinto, P. Schwarzbözl, R. Pitz-Paal "High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks." <i>Solar Energy</i> 218 (2021): 48-56, doi: <a href="https://doi.org/10.1016/j.solener.2021.01.046">10.1016/j.solener.2021.01.046</a>.</li>
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