In-Context Memory Along Airfoil Polars

FoilCORE

Abstract

Fast lift and drag estimates along airfoil polars matter for design sweeps, but high-fidelity tools are slow at scale. We show that a causal sequence model with only 6394 trainable param- eters can match or beat two public NeuralFoil checkpoints (xxsmall and xxxlarge) on the same withheld BigFoil-derived test rows when each method sees identical preprocessing and the decoder is fed ground-truth past coefficients along the polar (teacher forcing). On the first 4096 rows of that split, pooled mean Cl MAE is 0.051 54 and Cd MAE is 0.002 99, below both baselines at much smaller width.

FoilCORE walks the polar in angle-of-attack order. Geometry and (α, Re, M, Cl, Cd) tokens embed at width d=8. A masked causal trunk, path nonlinearities, gated outer products, and a two-output head predict the next step, so the curve is an ordered sweep rather than unrelated (α, Re) samples.

Greedy decoding with K=0 raises validation MAE versus teacher forcing; Section 7.4 describes a NeuralFoil xxxlarge warmstart for open-loop use. Scope and caveats are in Section 9.