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cs.LG2025
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains
Shizheng Wen, Arsh Kumbhat, Levi Lingsch +4
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. D…
cs.LG2025
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains
Sepehr Mousavi, Shizheng Wen, Levi Lingsch +3
Learning the solution operators of PDEs on arbitrary domains is challenging due to the diversity of possible domain shapes, in addition to the often intricate underlying physics. W…