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Addressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models
Neural network subgrid stress models often have a priori performance that is far better than the a posteriori performance, leading to neural network models that look very promising a priori completely failing in a posteriori Large Eddy Simulations (LES). This performance gap can be decreased by comb
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