Geometry-informed neural operator transformer for partial differential equations on arbitrary geometries

• A Geometry-Informed Neural Operator Transformer (GINOT) is proposed for forward predictions on arbitrary geometries. • GINOT encodes surface point clouds that are unordered, have non-uniform point density, and varying numbers of points. • GINOT effectively processes complex, arbitrary geometries and varying input conditions with good predictive accuracy. Machine-learning-based surrogate models offer significant computational efficiency and faster simulations compared to traditional numerical methods, especially for problems requiring repeated evaluations of partial differential equations. This work introduces the Geometry-Informed Neural Operator Transformer (GINOT), which integrates the transformer architecture with the neural operator framework to enable forward predictions on arbitrary geometries. GINOT employs a sampling and grouping strategy together with an attention mechanism to encode surface point clouds that are unordered, exhibit non-uniform point densities, and contain varying numbers of points for different geometries. The geometry information is seamlessly integrated with query points in the solution decoder through the attention mechanism. The performance of GINOT is validated on multiple challenging datasets, showcasing its accuracy and generalization capabilities for complex and arbitrary 2D and 3D geometries.