A neural network method for scalar conservation laws with convergence rates for shock waves
We propose a new entropy-compatible neural network method for scalar hyperbolic conservation laws and establish, to our knowledge, the first explicit $L^1$ convergence rates in this setting for piecewise smooth entropy solutions, including those with discontinuities. The method is based on a computable approximation of the Kružkov entropy residual that sits between the strong and weak forms of the entropy inequality. For a single regular shock in one dimension and finitely many smooth shock hypersurfaces with transversal interactions in several dimensions, we construct explicit neural networks with provably small loss by combining shock-adapted continuous piecewise linear functions with known approximation properties of $\tanh$ neural networks. The one-dimensional analysis also covers rarefactions, compound waves, regular shock interactions, and nondegenerate shock formation from smooth initial data. Together with entropy-based stability estimates, this gives rigorous $L^1$ error bounds for minimizers of the proposed loss. In particular, when the network size grows in proportion to the number of degrees of freedom of a space--time mesh of size $h$, the analysis recovers the classical Kuznetsov rate $O(h^{1/2})$ in shock-dominated cases. Numerical experiments in one and two space dimensions illustrate the performance of the method.