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The data it learned from Instead of sticking to one type of fluid or system, the team pulled together 1.8 TB of simulations covering many different scenarios: calm flows, turbulent flows, heat transfer, fluids going around obstacles, even two-phase flows through porous material. Variable Δt sub-sampling and per-dataset normalization encourage in-context inference across scales.
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