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Scale-Split Neural Operator for Memory- and Data-Efficient 3D Turbulence Prediction
Neural surrogates have emerged as fast alternatives to the numerical simulation of three-dimensional turbulence. However, training them at high resolution remains challenging, since the memory of full-field models grows with the resolution. In addition, full-resolution training data are expensive to
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