High-fidelity simulations of time-dependent nonlinear partial differential equations (PDEs) are expensive to repeat across families of domains and parameter fields, a cost that recurs in design optimization, uncertainty quantification, and computational medicine. Most neural-operator surrogates require each domain to be mapped onto a common grid or reference shape, and few predict the full time course. We introduce Graph Fourier Neural Kernels (G-FuNK), a neural operator for second-order semilinear PDEs with a diffusive principal part that transfers across geometries and anisotropic diffusion tensors without coordinate maps or diffeomorphisms. A weighted graph and its Laplacian supply a spectral basis that encodes geometry and anisotropy. Shared layers act in this basis, at a cost linear in mesh size after one partial eigendecomposition per geometry, and a neural ODE integrator propagates the learned generator to full spatiotemporal trajectories on unseen domains. Across anisotropic heat flow, stiff reaction-diffusion, FitzHugh-Nagumo, and patient-specific left-atrial electrophysiology on CT-derived surfaces, G-FuNK reproduces held-out trajectories with relative $L^2$ error between 0.046 and 0.160. Its accuracy is comparable to that of a geometry-aware Fourier neural operator on regular domains, and its predictions are unchanged when a rectangular domain is rotated. On left atria, whose venous and valvular openings prevent mapping onto a reference shape, it predicts an 85-ms trajectory in under half a second, against about 5 minutes for finite elements, with a mean activation-time error of 1.73 ms. Accuracy depends on graph construction and is highest when the learned generator is integrated at its training time step.

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