Towards a Physics Foundation Model

Model in one glance — Think of GPhyT as a hybrid of a neural net and a physics engine. It takes in a short history of what’s happening (like a few frames of a simulation), figures out the rules of change from that, then applies a simple update step to predict what comes next. It’s like teaching a transformer to play physics frame prediction with hints from basic calculus.

Data and scaling — Instead of sticking to one type of fluid or system, the team pulled together 1.8 TB of simulations covering many different scenarios: calm flows, turbulent flows, heat transfer, fluids going around obstacles, even two-phase flows through porous material. They also mixed up the time steps and normalized scales so the model learns how to adapt, not just memorize.

Multi-physics accuracy — On single-step forecasts across all test sets, GPhyT cuts median MSE vs. UNet by about 5× and vs. FNO by about 29× at similar parameter counts. They show average and median MSE improvements, with qualitative panels indicating sharper shocks and plumes than baselines.

Zero-shot generalization — With only a prompt of prior states, the model adapts to novel boundaries and even unseen physics. They report near-parity error when switching known periodic to open boundaries, and physically plausible bow shocks for supersonic flow plus structure in a turbulent radiative layer.

Long-range rollouts — Autoregressive predictions stay stable over 50 steps, retaining coherent global structures though fine detail diffuses over time.

Limits and knobs — Current scope is 2D fluids and heat transfer at fixed 256×128 resolution; extending to 3D, broader physics, and better long-term stability remains open. Prompt design matters: increasing temporal context helps, and using larger temporal patches trades small accuracy for big compute savings.