A third-order finite difference weighted essentially non-oscillatory scheme with shallow neural network
This work addresses the challenge of improving numerical simulations for hyperbolic conservation laws, which is incremental as it builds on existing WENO methods with neural network enhancements.
The authors tackled the problem of designing a finite difference weighted essentially non-oscillatory (WENO) scheme for hyperbolic conservation laws by integrating a shallow neural network, resulting in outperformed results in one-dimensional examples and improved behavior in two-dimensional examples compared to existing WENO3-JS and WENO3-Z schemes.
In this paper, we introduce the finite difference weighted essentially non-oscillatory (WENO) scheme based on the neural network for hyperbolic conservation laws. We employ the supervised learning and design two loss functions, one with the mean squared error and the other with the mean squared logarithmic error, where the WENO3-JS weights are computed as the labels. Each loss function consists of two components where the first component compares the difference between the weights from the neural network and WENO3-JS weights, while the second component matches the output weights of the neural network and the linear weights. The former of the loss function enforces the neural network to follow the WENO properties, implying that there is no need for the post-processing layer. Additionally the latter leads to better performance around discontinuities. As a neural network structure, we choose the shallow neural network (SNN) for computational efficiency with the Delta layer consisting of the normalized undivided differences. These constructed WENO3-SNN schemes show the outperformed results in one-dimensional examples and improved behavior in two-dimensional examples, compared with the simulations from WENO3-JS and WENO3-Z.