A physics foundation model finetuned on idealized simulations transfers zero-shot to laboratory turbulence, crossing a decades-old sim-experiment gap in the mixing growth rate.
To usher in a new class of machine learning for scientific data, building models that can leverage shared concepts across disciplines. We aim to develop, train, and release such foundation models for use by researchers worldwide.
