PDE Solvers Should Be Local: Fast, Stable Rollouts with Learned Local Stencils
This addresses the problem of inefficient and inaccurate neural PDE solvers for researchers and practitioners in scientific computing, offering a novel architecture that is incremental in combining finite-difference concepts with neural networks.
The paper tackles the problem of neural operator models for solving PDEs oversmoothing local dynamics and being computationally expensive by introducing FINO, a finite-difference-inspired neural architecture that enforces strict locality with learnable stencils and time-stepping. The result is up to 44% lower error and around 2× speedups over state-of-the-art baselines across multiple benchmarks.
Neural operator models for solving partial differential equations (PDEs) often rely on global mixing mechanisms-such as spectral convolutions or attention-which tend to oversmooth sharp local dynamics and introduce high computational cost. We present FINO, a finite-difference-inspired neural architecture that enforces strict locality while retaining multiscale representational power. FINO replaces fixed finite-difference stencil coefficients with learnable convolutional kernels and evolves states via an explicit, learnable time-stepping scheme. A central Local Operator Block leverage a differential stencil layer, a gating mask, and a linear fuse step to construct adaptive derivative-like local features that propagate forward in time. Embedded in an encoder-decoder with a bottleneck, FINO captures fine-grained local structures while preserving interpretability. We establish (i) a composition error bound linking one-step approximation error to stable long-horizon rollouts under a Lipschitz condition, and (ii) a universal approximation theorem for discrete time-stepped PDE dynamics. (iii) Across six benchmarks and a climate modelling task, FINO achieves up to 44\% lower error and up to around 2\times speedups over state-of-the-art operator-learning baselines, demonstrating that strict locality with learnable time-stepping yields an accurate and scalable foundation for neural PDE solvers.