A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling

ArXi:2506.07969v2 Announce Type: replace We consider the problem of modeling high-speed flows using machine learning methods. While most prior studies focus on low-speed fluid flows in which uniform time-stepping is practical, flows approaching and exceeding the speed of sound exhibit sudden changes such as shock waves. In such cases, it is essential to use adaptive time-stepping methods to allow a temporal resolution sufficient to resolve these phenomena while simultaneously balancing computational costs.