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

• URL: http://arxiv.org/abs/2506.07969v1
• Date: Mon, 09 Jun 2025 17:44:20 GMT
• Title: A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling
• Authors: Jacob Helwig, Sai Sreeharsha Adavi, Xuan Zhang, Yuchao Lin, Felix S. Chim, Luke Takeshi Vizzini, Haiyang Yu, Muhammad Hasnain, Saykat Kumar Biswas, John J. Holloway, Narendra Singh, N. K. Anand, Swagnik Guhathakurta, Shuiwang Ji,
• Abstract summary: We propose a two-phase machine learning method, known as ShockCast, to model high-speed flows with adaptive time-stepping.<n>As ShockCast is the first framework for learning high-speed flows, we evaluate our methods by generating two supersonic flow datasets.
• Score: 43.004155638491156
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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. Here, we propose a two-phase machine learning method, known as ShockCast, to model high-speed flows with adaptive time-stepping. In the first phase, we propose to employ a machine learning model to predict the timestep size. In the second phase, the predicted timestep is used as an input along with the current fluid fields to advance the system state by the predicted timestep. We explore several physically-motivated components for timestep prediction and introduce timestep conditioning strategies inspired by neural ODE and Mixture of Experts. As ShockCast is the first framework for learning high-speed flows, we evaluate our methods by generating two supersonic flow datasets, available at https://huggingface.co/datasets/divelab. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).

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