Poseidon

Walrus: A 1.3B-Parameter General Transformer for Fluid Dynamics Simulation

Polymathic AI Collaboration released Walrus, a 1.3 billion-parameter transformer model that simulates fluids, gases, and plasmas across 19 physical domains, outperforming prior specialized physics models. The model addresses aliasing artifacts in transformers—errors that compound at specific spatial locations over time—by randomly jittering input data at each time step before encoding, distributing errors rather than allowing accumulation. Walrus achieved lowest VRMSE in 18 of 19 domains for one-step predictions, reducing error by 63.6% on average versus best competing models. The jittering technique may generalize to vision and video transformer architectures where similar pixelation artifacts occur.