Addressing a posteriori performance degradation in neural network subgrid stress models
TL;DR
This work has shown that by ablating away the higher-order terms input into the neural network, the a priori versus a posteriori performance changes become less apparent and when combined, neural networks that use both training data augmentation and a less complex set of inputs have a posteriori performance more reflective of their a priori evaluation.
Abstract
Abstract Content of image described in text. 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 (LESs). 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 filter shapes before training the neural networks has no performance degradation a priori as compared with a neural network trained with one filter. A posteriori, neural networks trained with two different filters are more robust across two LES codes with different high-order 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 more reflective of their a priori evaluation.
