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This paper introduces AeroJEPA, a Joint-Embedding Predictive Architecture for scalable 3D aerodynamic field modeling. It addresses limitations in current surrogate models by predicting semantic latent representations of flow fields, enabling efficient high-fidelity analysis and design optimization.
This paper introduces MeLISA, a latent-free autoregressive generative surrogate for forecasting high-dimensional physical dynamics that uses pixel-space MeanFlow to achieve efficient one-step generation. It demonstrates superior long-horizon statistical accuracy and inference speed compared to neural operators on turbulent flow benchmarks.
DeepMind researchers discovered new families of unstable singularities in fundamental fluid dynamics equations using AI techniques, potentially advancing understanding of century-old mathematical problems like the Navier-Stokes equations. The work collaborates with Brown, NYU, and Stanford, revealing patterns in blow-up behavior with unprecedented computational accuracy.