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.

-- In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self - representative Concept Factorization Network (DSCF - Net), for clustering deep feature s . To improve the representation and clustering abilities, DSCF - Net explicitl y considers discovering hidden deep semantic features, enhancing the robustness properties of the deep factorization to noise and preserving the local manifold structures of deep features. To discover hidden deep representations, DSCF - Net designs a hierarchical factorization architecture using multiple layers of li near transformations, where the hierarchical representation is performed by formulating the problem as optimizing the basis concepts in each layer to improve the representation indirectly. DSCF - Net also improves the robustness by subspace recovery for spar se error correction firstly and then performs the deep factorization in the recovered visual subspace. To obtain locality - preserving representations, we also present an adaptive deep self - representative weighting strategy by using the coefficient matrix as the adaptive reconstruction weights to keep the locality of representation s . Extensive comparison results with several other related models show that DSCF - Net delivers state - of - the - art performance on several public databases. R epresentation learning from h igh - dimensional complex data is always an important and fundamental problem in the fields of pattern recognition an d data mining [40 - 50 ] .