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原文摘要

Most physical systems are governed by nonlinear partial differential equations, yet the analytical tools that make linear systems transparent, such as eigenspectra, dispersion relations and modal decomposition, remain unavailable in nonlinear settings. We introduce Linear Generator Network-Koopman (LGN-KM), a neural operator that learns a nonlinear lift and a continuous-time linear generator for the lifted dynamics through the decomposition $L_k = S - D_k$, where $S$ is skew-symmetric (conservative coupling) and $D_k$ is positive-definite diagonal (mode-selective dissipation). The decomposition guarantees stability by construction and exposes the eigenstructure directly, making linear-systems analysis available on a nonlinear PDE. On 2D Navier-Stokes turbulence, the generator yields a complete multi-branch dispersion relation and models trained independently at viscosities $100\times$ apart learn the same coupling spectrum ($R^2 = 0.87$). The same architecture shows the corresponding diffusive damping on FitzHugh-Nagumo reaction-diffusion, and for pulse propagation in optical fibers, the oscillation frequencies of the learned generator match the fiber's dispersion relation, giving the group-velocity dispersion to within $3.1\%$ with no fitted constant. The architecture additionally enables long-horizon stability, $O(1)$ continuous-time evaluation, and physics-informed cross-regime model transfer.