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Addressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models

arxiv.org/abs/2511.17475

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Updated 5 h ago · first seen 11 Sept 2026

paper_01M294FSY3P5ZGKE6P9DKQYGA7

Published
11 Sept 2026
T1 · 5 h ago
arXiv
2511.17475
T1 · 5 h ago
Category
physics.flu-dyn
T1 · 5 h ago

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-cross Abstract: 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 combining two different methods, training data augmentation and reducing input complexity to the neural network. Augmenting the training data with two different filters before training the neural networks has no performance degradation a priori as compared to a neural network trained with one filter. A posteriori, neural networks trained with two different filters are far more robust across two different LES codes with different numerical schemes. In addition, by ablating away the higher order terms input into the neural network, the a priori versus a posteriori performance changes become less apparent. When combined, neural networks that use both training data augmentation and a less complex set of inputs have a posteriori performance far more reflective of their a priori evaluation. → currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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