Neural Network Tools for Shock Wave Decomposition

This article explores advanced methods for simulating shock waves using non-diffusive neural networks (NDNNs). It explains how AI techniques can handle shock wave generation without requiring prior knowledge of their location, model interactions between multiple waves, and efficiently decompose initial wave states for Riemann problems. By leveraging neural networks to approximate shock and rarefaction curves, the approach provides a scalable solution for complex one-dimensional systems of conservation laws, offering improved accuracy and efficiency over traditional numerical solvers.