SPFNO: Spectral operator learning for PDEs with Dirichlet and Neumann boundary conditions

Neural operators have been validated as promising deep surrogate models for solving partial differential equations (PDEs). Despite the critical role of boundary conditions in PDEs, however, only a limited number of neural operators robustly enforce these conditions. In this paper we introduce semi-periodic Fourier neural operator (SPFNO), a novel spectral operator learning method, to learn the target operators of PDEs with non-periodic BCs. This method extends our previous work (arXiv:2206.12698), which showed significant improvements by employing enhanced neural operators that precisely satisfy the boundary conditions. However, the previous work is associated with Gaussian grids, restricting comprehensive comparisons across most public datasets. Additionally, we present numerical results for various PDEs such as the viscous Burgers' equation, Darcy flow, incompressible pipe flow, and coupled reactiondiffusion equations. These results demonstrate the computational efficiency, resolution invariant property, and BC-satisfaction behavior of proposed model. An accuracy improvement of approximately 1.7X-4.7X over the non-BC-satisfying baselines is also achieved. Furthermore, our studies on SOL underscore the significance of satisfying BCs as a criterion for deep surrogate models of PDEs.