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                <term xml:lang="en">Machine Learning</term>
                <term xml:lang="en">Physics informed machine learning</term>
                <term xml:lang="en">Numerical simulation</term>
                <term xml:lang="en">Airfoil</term>
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              <p>The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computational fluid dynamics (CFD). Yet, persistent challenges in accuracy, generalization, and physical consistency hinder the practical deployment of ML models in scientific domains. To address these limitations and systematically benchmark progress, we organized the ML4CFD competition, centered on surrogate modeling for aerodynamic simulations over two-dimensional airfoils. The competition attracted over 240 teams, who were provided with a curated dataset generated via OpenFOAM and evaluated through a multi-criteria framework encompassing predictive accuracy, physical fidelity, computational efficiency, and out-of-distribution generalization. This retrospective analysis reviews the competition outcomes, highlighting several approaches that outperformed baselines under our global evaluation score. Notably, the top entry exceeded the performance of the original OpenFOAM solver on aggregate metrics, illustrating the promise of MLbased surrogates to outperform traditional solvers under tailored criteria. Drawing from these results, we analyze the key design principles of top submissions, assess the robustness of our evaluation framework, and offer guidance for future scientific ML challenges.</p>
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              <addrLine>Siège social : 14 place Georges Pompidou – 78180 Montigny-Le-Bretonneux – France</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.ansys.com/</ref>
          </desc>
          <listRelation>
            <relation active="#struct-1207958" type="direct"/>
          </listRelation>
        </org>
        <org type="institution" xml:id="struct-505477" status="VALID">
          <orgName>Safran Tech</orgName>
          <desc>
            <address>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.safran-group.com/fr</ref>
          </desc>
        </org>
        <org type="institution" xml:id="struct-1060418" status="VALID">
          <orgName>Ant Group</orgName>
          <desc>
            <address>
              <country key="CN"/>
            </address>
          </desc>
        </org>
        <org type="institution" xml:id="struct-55697" status="VALID">
          <orgName>Airbus [France]</orgName>
          <desc>
            <address>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.airbus.com/company/worldwide-presence/airbus-in-france/</ref>
          </desc>
        </org>
        <org type="institution" xml:id="struct-82720" status="VALID">
          <idno type="ROR">https://ror.org/04gyj6s21</idno>
          <orgName>Institut Supérieur de l'Aéronautique et de l'Espace</orgName>
          <orgName type="acronym">ISAE-SUPAERO</orgName>
          <desc>
            <address>
              <addrLine>ISAE - 10 av. Edouard Belin - BP 54032 - 31055 TOULOUSE Cedex 4</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.isae-supaero.fr/fr/</ref>
          </desc>
        </org>
        <org type="regroupinstitution" xml:id="struct-567074" status="VALID">
          <orgName>Texas A&amp;M University System</orgName>
          <desc>
            <address>
              <country key="US"/>
            </address>
            <ref type="url">https://www.tamus.edu/</ref>
          </desc>
        </org>
        <org type="regroupinstitution" xml:id="struct-1207958" status="VALID">
          <orgName>ANSYS Inc. (États-Unis)</orgName>
          <date type="start">1970-01-01</date>
          <desc>
            <address>
              <addrLine>Canonsburg, Pennsylvanie, États-Unis</addrLine>
              <country key="US"/>
            </address>
            <ref type="url">https://www.ansys.com/</ref>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>