Neural operators collapse to the conditional mean on stochastic PDEs. The Martingale Neural Operator uses the Doob–Meyer decomposition to split outputs into predictable drift and martingale components, recovering the full terminal distribution — cutting Wasserstein distance up to 120× on φ⁴ field theory and 68× on stochastic Burgers.
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