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This research aims to address the limitations of scaling and efficiency in mesh simulations using Graph Neural Networks.

June 4, 2026Neural Computing and Applications0 citationsOpen Access

Training Transformers for Enhanced Mesh-Based Simulations

Training transformers for mesh-based simulations

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Randomized trial evaluates a novel Graph Transformer for scaling mesh-based simulations, indicating significant efficiency improvements.

This research aims to address the limitations of scaling and efficiency in mesh simulations using Graph Neural Networks.
Proposed a Graph Transformer architecture utilizing the adjacency matrix as an attention mask.
Incorporated augmentations like Dilated Sliding Windows and Global Attention for computational efficiency.
Conducted extensive experiments using complex 3D computational fluid dynamics datasets and trained over 60 models.
The smallest model is 7× faster and 6× smaller than MeshGraphNet, achieving similar performance.
The largest model outperforms previous state-of-the-art by 38.8% on average.
Demonstrated remarkable scalability on meshes with up to 300k nodes and 3 million edges.

Garnier et al. (2026) studied this question.

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June 4, 2026Neural Computing and Applications0 citationsOpen Access

Training transformers for mesh-based simulations

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial evaluates a novel Graph Transformer for scaling mesh-based simulations, indicating significant efficiency improvements.

This research aims to address the limitations of scaling and efficiency in mesh simulations using Graph Neural Networks.
Proposed a Graph Transformer architecture utilizing the adjacency matrix as an attention mask.
Incorporated augmentations like Dilated Sliding Windows and Global Attention for computational efficiency.
Conducted extensive experiments using complex 3D computational fluid dynamics datasets and trained over 60 models.
The smallest model is 7× faster and 6× smaller than MeshGraphNet, achieving similar performance.
The largest model outperforms previous state-of-the-art by 38.8% on average.
Demonstrated remarkable scalability on meshes with up to 300k nodes and 3 million edges.

Garnier et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark