A geometry-conditioned FNO is trained on pseudospectral data to approximate the one-step operator for cubic NLS on 2D tori and reproduces distinct H²-norm growth on rational versus irrational aspect ratios.
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Operator Learning for Cubic Nonlinear Schr\"odinger Equation on Periodic Domains
A geometry-conditioned FNO is trained on pseudospectral data to approximate the one-step operator for cubic NLS on 2D tori and reproduces distinct H²-norm growth on rational versus irrational aspect ratios.