2025
Geometry Aware Operator Transformer as an efficient and accurate neural surrogate for PDEs on arbitrary domains
NeurIPS 2025poster
The very challenging task of learning solution operators of PDEs on arbitrary domains accurately and efficiently is of vital importance to engineering and industrial simulations. Despite the existence of many operator learning algorithms to approximate such PDEs, we find that accurate models are not…