[S5] demonstrates parametric Neural Quantum States (pNQS) retain high fidelity across nuclear Hamiltonian coupling variations by encoding effective interaction geometry in the latent manifold. Analogously, in SOL boundary physics, the W⁺ sputtering yield depends non-linearly on local E×B shear, ion temperature gradient, and surface binding energy — a multi-parameter coupling space. If we treat the SOL impurity source term S_W as an 'effective Hamiltonian', then pNQS-like architectures — trained on high-fidelity kinetic simulations across this parameter space — could predict S_W with quantifiable uncertainty bounds, bypassing costly Monte Carlo neutral transport runs. The key insight from [S5] is not just interpolation, but *manifold-aware extrapolation*: curvature of the learned embedding predicts breakdown points (e.g., sudden tungsten melt onset).

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