Physics Foundation Models for Efficient Control of Fluid Flows

Key points

• We construct a PFM-based controller by attaching lightweight Gaussian actor and critic heads to frozen pretrained representations and train the policy using proximal policy optimization (PPO).1
• We evaluate the approach on HydroGym's two-dimensional Kolmogorov-flow environment using vorticity observations at \(64\times64\) and \(128\times128\) resolutions and Reynolds numbers \(Re=100\) and \(200\).1
• Across these settings, we compare the frozen PFM controller against a baseline MLP policy trained with the same PPO framework.1
• These results provide initial evidence that representations learned through self-supervised next-frame prediction can be transferred directly to closed-loop fluid flow control.1

Sources (1)

• [1]Physics Foundation Models for Efficient Control of Fluid Flows
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 08:51 PM

  We investigate whether a physics foundation model (PFM) pretrained for PDE forecasting can be repurposed for closed-loop fluid-flow control.

  We construct a PFM-based controller by attaching lightweight Gaussian actor and critic heads to frozen pretrained representations and train the policy using proximal policy optimization (PPO).

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