Geometry-Informed Neural Operator Transformer

• URL: http://arxiv.org/abs/2504.19452v2
• Date: Tue, 29 Apr 2025 15:30:04 GMT
• Title: Geometry-Informed Neural Operator Transformer
• Authors: Qibang Liu, Vincient Zhong, Hadi Meidani, Diab Abueidda, Seid Koric, Philippe Geubelle,
• Abstract summary: This work introduces the Geometry-Informed Neural Operator Transformer (GINOT), which integrates the transformer architecture with the neural operator framework to enable forward predictions for arbitrary geometries.<n>The performance of GINOT is validated on multiple challenging datasets, showcasing its high accuracy and strong generalization capabilities for complex and arbitrary 2D and 3D geometries.
• Score: 0.8906214436849201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 for arbitrary geometries. GINOT encodes the surface points cloud of a geometry using a sampling and grouping mechanism combined with an attention mechanism, ensuring invariance to point order and padding while maintaining robustness to variations in point density. 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 high accuracy and strong generalization capabilities for complex and arbitrary 2D and 3D geometries.

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