Elie Hachem

Autoregressive graph surrogates fail to generalize over long horizons without explicit level-set geometric encodings, while divergence-free physics regularization proves largely negligible in laminar flow but non-negotiable under turbulence.

Heuristic learning with coding agents outperforms traditional DRL in active flow control while providing interpretable and physically meaningful feedback mechanisms.