Shocks, Collisions, and Entropy—Neural Networks Handle It All

This article explores how physics-informed neural networks (PINNs) can simulate shock wave generation, interactions, and entropy solutions. Using Burgers’ equation as a test case, the models accurately handle wave formation, collisions, and rarefaction without prior knowledge of origin points. The results highlight how deep learning can advance computational fluid dynamics by tackling problems once limited to traditional numerical methods.