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This research demonstrates the viability of repurposing physics foundation models pretrained on partial differential equation forecasting for closed-loop fluid flow control tasks. By freezing pretrained representations and training lightweight policy heads, the approach achieves superior performance with reduced control effort compared to standard MLP baselines in Kolmogorov-flow environments. The study provides early evidence that self-supervised representations learned for prediction can be directly transferred to complex control problems without fine-tuning the core model weights.
Physics Foundation Models for Efficient Control of Fluid Flows
arXiv:2610.15173v1 Announce Type: cross Abstract: 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). 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 \(\mathrm{Re}=100\) and \(200\). Across these settings, we compare the frozen PFM controller against a baseline MLP policy trained with the same PPO framework. The PFM controller achieves consistently higher reward while requiring smaller executed action magnitudes over training, indicating improved control performance with reduced control effort. These results provide initial evidence that representations learned through self-supervised next-frame prediction can be transferred directly to closed-loop fluid flow control. Project page: https://mahindrautela.github.io/morph.
Abstract: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). 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 \(\mathrm{Re}=100\) and \(200\). Across these settings, we compare the frozen PFM controller against a baseline MLP policy trained with the same PPO framework. The PFM controller achieves consistently higher reward while requiring smaller executed action magnitudes over training, indicating improved control performance with reduced control effort. These results provide initial evidence that representations learned through self-supervised next-frame prediction can be transferred directly to closed-loop fluid flow control. Project page: this https URL.
Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
arXiv:2610.15173 [physics.flu-dyn]
(or arXiv:2610.15173v1 [physics.flu-dyn] for this version)
https://doi.org/10.48550/arXiv.2610.15173 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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