Addressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models

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.

References in corpus (11)

• A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
• Deep Neural Networks for Data-Driven Turbulence Models
• Perspectives on Machine Learning-augmented Reynolds-averaged and Large Eddy Simulation Models of Turbulence
• Scientific multi-agent reinforcement learning for wall-models of turbulent flows
• Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations
• Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES
• Deep reinforcement learning for large-eddy simulation modeling in wall-bounded turbulence
• Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodies
• Neural-network-based mixed subgrid-scale model for turbulent flow
• A recursive neural-network-based subgrid-scale model for large eddy simulation: application to homogeneous isotropic turbulence
• Subgrid Stress Modelling with Multi-dimensional State Space Sequence Models