{"ID":2921875,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T21:58:08.67546302Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01470","arxiv_id":"2606.01470","title":"Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence","abstract":"Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Rayleigh-Taylor instability (RTI), a ubiquitous and demanding fluid instability seen from tabletop flows to supernova explosions, in which small perturbations at a density interface grow into chaotic, multiscale mixing as a lighter fluid accelerates into a heavier one. Standard ML models struggle with RTI, and despite over a century of theoretical, numerical, and experimental work, it carries an unresolved discrepancy between simulation and experiment: the late-time mixing growth rate, $α$, measured in most laboratory experiments ($\\sim$ 0.06-0.07), is roughly three times the value from idealized direct numerical simulations (DNS, $\\sim$ 0.02). The gap's origin remains debated. These properties make RTI a stringent test for a question that matters well beyond RTI: can foundation models trained only on simulations generalise to sparse, messy, and noisy laboratory settings? We finetune Walrus, a foundation model for continuum dynamics, on three or fewer DNS realizations and recover key RTI physics over long rollouts. Applied zero-shot to sliding-barrier laboratory data, the finetuned model leaves the DNS-like regime and enters the observed growth band, having never seen a single experimental sample. These results provide independent, data-driven evidence that initial conditions play a crucial role in the longstanding sim-experiment gap in $α$. The model also generalises zero-shot to stable stratification, a buoyancy regime absent from training, correctly slowing mixing-layer growth. Together, our results show that foundation models can generalise well beyond their training data, predicting laboratory behavior and unseen physical regimes, opening new ways to probe longstanding simulation-experiment gaps.","short_abstract":"Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Rayleigh-Taylor instability (RTI), a ubiquitous and demanding fluid instability seen from tabletop flows to supernova explosions, in which smal...","url_abs":"https://arxiv.org/abs/2606.01470","url_pdf":"https://arxiv.org/pdf/2606.01470v1","authors":"[\"Payel Mukhopadhyay\",\"Stefan S. Nixon\",\"Romain Watteaux\",\"Michael McCabe\",\"Alberto Bietti\",\"Kyunghyun Cho\",\"Cristiana Diaconu\",\"Irina Espejo Morales\",\"David Fouhey\",\"Siavash Golkar\",\"Tom Hehir\",\"Shirley Ho\",\"Jake Kovalic\",\"Geraud Krawezik\",\"Francois Lanusse\",\"Tanya Marwah\",\"Rudy Morel\",\"Mariel Pettee\",\"Helen Qu\",\"Jeff Shen\",\"Hadi Sotoudeh\",\"Stuart B. Dalziel\",\"Miles Cranmer\"]","published":"2026-05-31T22:07:38Z","proceeding":"physics.flu-dyn","tasks":"[\"physics.flu-dyn\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}