<ul><li>The integration of machine learning (ML) into physical sciences is transforming computational paradigms, aiming to enhance simulations like computational fluid dynamics (CFD).</li><li>ML4CFD competition was organized to improve accuracy, generalization, and physical consistency of ML models for aerodynamic simulations over two-dimensional airfoils.</li><li>Over 240 teams participated in the competition, utilizing a dataset from OpenFOAM and evaluated based on predictive accuracy, physical fidelity, computational efficiency, and generalization.</li><li>Retrospective analysis of the competition revealed top-performing approaches surpassing traditional solvers, showcasing the potential of ML-based surrogates in scientific simulations.</li></ul>

NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis

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