提升神经网络亚格子应力模型的后验性能稳定性
Addressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models
- 用双滤波数据增强训练，降低输入复杂度
- 后验模拟中跨代码鲁棒性显著提升
- 适合从事大气/海洋模拟的研究者
神经网络亚格子应力模型常表现出先验性能远优于后验性能的问题，导致在大涡模拟（LES）中表现不佳。通过结合训练数据增强与降低输入复杂度的方法，可有效缩小这一差距。使用两种不同滤波方式增强训练数据，先验性能无损失；后验性能在采用不同数值格式的两个不同LES代码中均更稳定。进一步去除输入中的高阶项后，先验与后验性能差异减弱。两者结合后，模型后验表现更接近其先验评估结果。
原文摘要 · Abstract (English)
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.
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