A PINN-based mapping generates custom structured grids for irregular domains, enabling PDE solving with FDM and PICNN on rectangular grids.
A nodal ghost method based on variational formulation and regular square grid for elliptic problems on arbitrary domains in two space dimensions
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abstract
This paper focuses on the numerical solution of elliptic partial differential equations (PDEs) with Dirichlet and mixed boundary conditions, specifically addressing the challenges arising from irregular domains. Both finite element method (FEM) and finite difference method (FDM), face difficulties in dealing with arbitrary domains. The paper introduces a novel nodal symmetric ghost {method based on a variational formulation}, which combines the advantages of FEM and FDM. The method employs bilinear finite elements on a structured mesh and provides a detailed implementation description. A rigorous a priori convergence rate analysis is also presented. The convergence rates are validated with many numerical experiments, in both one and two space dimensions.
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Physics-Informed Neural Networks for Irregular Domain Mapping and Partial Differential Equations solving
A PINN-based mapping generates custom structured grids for irregular domains, enabling PDE solving with FDM and PICNN on rectangular grids.