# Procedural Prior Pretraining

An engineering design study holds only tens to hundreds of simulations, each on its own mesh, and conventional surrogates are retrained for every study. PhysICL is pretrained once on a procedural prior, then treats a few labeled simulations as context and predicts fields, with quantiles, directly at each query's own unstructured-mesh nodes in one forward pass, with no weight update. On five PLAID datasets unseen in pretraining its field error falls as context grows and drops below the nearest context simulation; on AhmedML surfaces its peak GPU memory stays at 4.27 GiB.

An engineering design study holds only tens to hundreds of simulations, each on its own mesh, and conventional surrogates are retrained for every study. PhysICL is pretrained once on a procedural prior, then treats a few labeled simulations as context and predicts fields, with quantiles, directly at each query's own unstructured-mesh nodes in one forward pass, with no weight update. On five PLAID datasets unseen in pretraining its field error falls as context grows and drops below the nearest context simulation; on AhmedML surfaces its peak GPU memory stays at 4.27 GiB.